Sams Teach Yourself SQL™ in 10 Minutes [3rd ed] 0672325675, 9780672325670

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Teach Yourself

SQL in Ben Forta

10 Minutes THIRD EDITION

800 East 96th Street, Indianapolis, Indiana 46240 USA

Sams Teach Yourself SQL in 10 Minutes, Third Edition

ASSOCIATE PUBLISHER

Copyright © 2004 by Sams Publishing

Mark Renfrow

All rights reserved. No part of this book shall be reproduced, stored in a retrieval system, or transmitted by any means, electronic, mechanical, photocopying, recording, or otherwise, without written permission from the publisher. No patent liability is assumed with respect to the use of the information contained herein. Although every precaution has been taken in the preparation of this book, the publisher and author assume no responsibility for errors or omissions. Nor is any liability assumed for damages resulting from the use of the information contained herein.

MANAGING EDITOR

International Standard Book Number: 0-672-32567-5

Leslie Joseph

Library of Congress Catalog Card Number: 2003093137 Printed in the United States of America

Michael Stephens

DEVELOPMENT EDITOR

Charlotte Clapp

PROJECT EDITOR Dan Knott

INDEXER Tom Dinse

PROOFREADER

TECHNICAL EDITOR Christopher McGee

First Printing: April 2004 10

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PUBLISHING COORDINATOR Cindy Teeters

Trademarks All terms mentioned in this book that are known to be trademarks or service marks have been appropriately capitalized. Sams Publishing cannot attest to the accuracy of this information. Use of a term in this book should not be regarded as affecting the validity of any trademark or service mark.

Warning and Disclaimer Every effort has been made to make this book as complete and as accurate as possible, but no warranty or fitness is implied. The information provided is on an “as is” basis. The author and the publisher shall have neither liability nor responsibility to any person or entity with respect to any loss or damages arising from the information contained in this book.

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INTERIOR DESIGNER Gary Adair

COVER DESIGNER Gary Adair

Contents Introduction..........................................................................................1

1

Understanding SQL

5

Database Basics ..................................................................................5 What Is SQL? ....................................................................................11 Try It Yourself ....................................................................................12 Summary ............................................................................................12

2

Retrieving Data

13

The SELECT Statement........................................................................13 Retrieving Individual Columns..........................................................14 Retrieving Multiple Columns ............................................................16 Retrieving All Columns ....................................................................17 Summary ............................................................................................18

3

Sorting Retrieved Data

19

Sorting Data ......................................................................................19 Sorting by Multiple Columns ............................................................21 Sorting by Column Position ..............................................................22 Specifying Sort Direction ..................................................................23 Summary ............................................................................................25

4

Filtering Data

26

Using the WHERE Clause......................................................................26 The WHERE Clause Operators ..............................................................28 Summary ............................................................................................32

5

Advanced Data Filtering

33

Combining WHERE Clauses ................................................................33 Using the IN Operator........................................................................36 Using the NOT Operator ......................................................................38 Summary ............................................................................................39

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Sams Teach Yourself SQL in Ten Minutes

6

Using Wildcard Filtering

40

Using the LIKE Operator ....................................................................40 Tips for Using Wildcards ..................................................................46 Summary ............................................................................................47

7

Creating Calculated Fields

48

Understanding Calculated Fields ......................................................48 Concatenating Fields..........................................................................49 Performing Mathematical Calculations ............................................55 Summary ............................................................................................56

8

Using Data Manipulation Functions

57

Understanding Functions ..................................................................57 Using Functions ................................................................................59 Summary ............................................................................................65

9

Summarizing Data

66

Using Aggregate Functions................................................................66 Aggregates on Distinct Values ..........................................................72 Combining Aggregate Functions ......................................................73 Summary ............................................................................................74

10

Grouping Data

75

Understanding Data Grouping ..........................................................75 Creating Groups ................................................................................76 Filtering Groups ................................................................................78 Grouping and Sorting ........................................................................80 SELECT Clause Ordering ....................................................................83 Summary ............................................................................................83

11

Working with Subqueries

84

Understanding Subqueries ................................................................84 Filtering by Subquery ........................................................................84 Using Subqueries As Calculated Fields ............................................88 Summary ............................................................................................90

Contents

12

Joining Tables

v

91

Understanding Joins ..........................................................................91 Creating a Join ..................................................................................94 Summary ..........................................................................................100

13

Creating Advanced Joins

101

Using Table Aliases ........................................................................101 Using Different Join Types ..............................................................102 Using Joins with Aggregate Functions ............................................108 Using Joins and Join Conditions ....................................................110 Summary ..........................................................................................110

14

Combining Queries

111

Understanding Combined Queries ..................................................111 Creating Combined Queries ............................................................112 Summary ..........................................................................................117

15

Inserting Data

118

Understanding Data Insertion ..........................................................118 Copying from One Table to Another ..............................................124 Summary ..........................................................................................126

16

Updating and Deleting Data

127

Updating Data ..................................................................................127 Deleting Data ..................................................................................129 Guidelines for Updating and Deleting Data ....................................131 Summary ..........................................................................................132

17

Creating and Manipulating Tables

133

Creating Tables ................................................................................133 Updating Tables ..............................................................................139 Deleting Tables ................................................................................141 Renaming Tables..............................................................................141 Summary ..........................................................................................142

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18

Using Views

143

Understanding Views ......................................................................143 Creating Views ................................................................................146 Summary ..........................................................................................152

19

Working with Stored Procedures

153

Understanding Stored Procedures....................................................153 Why to Use Stored Procedures........................................................154 Executing Stored Procedures ..........................................................156 Creating Stored Procedures ............................................................157 Summary ..........................................................................................160

20

Managing Transaction Processing

161

Understanding Transaction Processing............................................161 Controlling Transactions..................................................................164 Summary ..........................................................................................167

21

Using Cursors

168

Understanding Cursors ....................................................................168 Working with Cursors......................................................................170 Summary ..........................................................................................173

22

Understanding Advanced SQL Features

174

Understanding Constraints ..............................................................174 Understanding Indexes ....................................................................180 Understanding Triggers....................................................................183 Database Security ............................................................................185 Summary ..........................................................................................186

A

Sample Table Scripts

187

Understanding the Sample Tables ..................................................187 Obtaining the Sample Tables ..........................................................191

B

Working in Popular Applications

194

Using Aqua Data Studio ..................................................................195 Using DB2 ......................................................................................195 Using Macromedia ColdFusion ......................................................196

Contents

Using Microsoft Access ..................................................................197 Using Microsoft ASP ......................................................................199 Using Microsoft ASP.NET ..............................................................199 Using Microsoft Query ....................................................................200 Using Microsoft SQL Server ..........................................................201 Using MySQL..................................................................................202 Using Oracle ....................................................................................202 Using PHP........................................................................................203 Using PostgreSQL ..........................................................................203 Using Query Tool ............................................................................203 Using Sybase....................................................................................204 Configuring ODBC Data Sources ..................................................205

C

SQL Statement Syntax

207

....................................................................................207 COMMIT ..............................................................................................208 CREATE INDEX ..................................................................................208 CREATE PROCEDURE ..........................................................................208 CREATE TABLE ..................................................................................208 CREATE VIEW ....................................................................................209 DELETE ..............................................................................................209 DROP ..................................................................................................209 INSERT ..............................................................................................209 INSERT SELECT ................................................................................210 ROLLBACK ..........................................................................................210 SELECT ..............................................................................................210 UPDATE ..............................................................................................211 ALTER TABLE

D

Using SQL Datatypes

212

String Datatypes ..............................................................................213 Numeric Datatypes ..........................................................................215 Date and Time Datatypes ................................................................216 Binary Datatypes..............................................................................217

E

SQL Reserved Words

219

Index

225

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About the Author Ben Forta is Macromedia’s Senior Technical Evangelist and has almost 20 years of experience in the computer industry in product development, support, training, and product marketing. Ben is the author of the bestselling ColdFusion Web Application Construction Kit and Advanced ColdFusion Development (both published by Que), Sams Teach Yourself Regular Expressions in 10 Minutes (in this same series), and also books on Flash, Java, WAP, Windows 2000, and other subjects. He has extensive experience in database design and development, has implemented databases for several highly successful commercial software programs, and is a frequent lecturer and columnist on Internet and database technologies. Born in London, England, and educated in London, New York, and Los Angeles, Ben now lives in Oak Park, Michigan, with his wife Marcy and their seven children. Ben welcomes your email at [email protected], and invites you to visit his Web site at http://www.forta.com.

Acknowledgments Thanks to the team at Sams for all these years of support, dedication, and encouragement. A special thank you to Mike Stephens and Mark Renfrow for shepherding this new edition from concept to reality (a process that required them to occasionally shepherd me, too). Thanks to the many hundreds of you who provided feedback on the first two editions of this book. Fortunately, most of it was positive, and all of it was appreciated. The enhancements and changes in this edition are a direct response to your feedback. And finally, thanks to the many thousands of you who bought the previous editions of this book (in English, and in any of the many translations), making it not just my best-selling title, but also one of the best-selling books on the subject. Your continued support is the highest compliment an author can ever be paid.

We Want to Hear from You! As the reader of this book, you are our most important critic and commentator. We value your opinion and want to know what we’re doing right, what we could do better, what areas you’d like to see us publish in, and any other words of wisdom you’re willing to pass our way. As an associate publisher for Sams Publishing, I welcome your comments. You can email or write me directly to let me know what you did or didn’t like about this book—as well as what we can do to make our books better. Please note that I cannot help you with technical problems related to the topic of this book. We do have a User Services group, however, where I will forward specific technical questions related to the book. When you write, please be sure to include this book’s title and author as well as your name, email address, and phone number. I will carefully review your comments and share them with the author and editors who worked on the book. Email:

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Michael Stephens Associate Publisher Sams Publishing 800 East 96th Street Indianapolis, IN 46240 USA

For more information about this book or another Sams Publishing title, visit our Web site at www.samspublishing.com. Type the ISBN (0672325675) or the title of this book in the Search field to find the page you’re looking for.

Introduction SQL is the most widely used database language. Whether you are an application developer, database administrator, Web application designer, or Microsoft Office user, a good working knowledge of SQL is an important part of interacting with databases. This book was born out of necessity. I had been teaching Web application development for several years, and students were constantly asking for SQL book recommendations. There are lots of SQL books out there. Some are actually very good. But they all have one thing in common: for most users they teach just too much information. Instead of teaching SQL itself most books teach everything from database design and normalization to relational database theory and administrative concerns. And while those are all important topics, they are not of interest to most of us who just need to learn SQL. And so, not finding a single book that I felt comfortable recommending, I turned that classroom experience into the book you are holding. Sams Teach Yourself SQL in 10 Minutes will teach you SQL you need to know, starting with simple data retrieval and working on to more complex topics including the use of joins, subqueries, stored procedures, cursors, triggers, and table constraints. You’ll learn methodically, systematically, and simply—in lessons that will each take 10 minutes or less to complete. Now in its third edition, this book has taught SQL to hundreds of thousands of users, and now it is your turn. So turn to Lesson 1, and get to work. You’ll be writing world class SQL in no time at all.

Who is the Teach Yourself SQL Book For? This book is for you if • You are new to SQL. • You want to quickly learn how to get the most out of SQL.

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Sams Teach Yourself SQL in Ten Minutes

• You want to learn how to use SQL in your own application development. • You want to be productive quickly and easily in SQL without having to call someone for help.

DBMSs Covered in This Book For the most part, the SQL taught in this book will apply to any Database Management System (DBMS). However, as all SQL implementations are not created equal, the following DBMSs are explicitly covered (and specific instructions or notes are included where needed): • IBM DB2 • Microsoft Access • Microsoft SQL Server • MySQL • Oracle • PostgreSQL • Sybase Adaptive Server Example databases and SQL scripts are also available for all of these DBMSs.

Conventions Used in This Book This book uses different typefaces to differentiate between code and regular English, and also to help you identify important concepts. Text that you type and text that should appear on your screen is presented in monospace type. It will look like this to mimic the way text looks on your screen.

Introduction

Placeholders for variables and expressions appear in monospace italic font. You should replace the placeholder with the specific value it represents. This arrow (➥) at the beginning of a line of code means that a single line of code is too long to fit on the printed page. Continue typing all the characters after the ➥ as though they were part of the preceding line.

A Note presents interesting pieces of information related to the surrounding discussion.

A Tip offers advice or teaches an easier way to do something.

A Caution advises you about potential problems and helps you steer clear of disaster.

New Term icons provide clear definitions of new, essential terms.

INPUT

The Input icon identifies code that you can type in yourself.

OUTPUT

The Output icon highlights the output produced by running a program. The Analysis icon alerts you to the author’s line-by-line

ANALYSIS analysis of a program.

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LESSON 1

Understanding SQL In this lesson, you’ll learn exactly what SQL is and what it will do for you.

Database Basics The fact that you are reading a book on SQL indicates that you, somehow, need to interact with databases. SQL is a language used to do just this, so before looking at SQL itself, it is important that you understand some basic concepts about databases and database technologies. Whether you are aware of it or not, you use databases all the time. Each time you select a name from your email address book, you are using a database. If you conduct a search on an Internet search site, you are using a database. When you log into your network at work, you are validating your name and password against a database. Even when you use your ATM card at a cash machine, you are using databases for PIN number verification and balance checking. But even though we all use databases all the time, there remains much confusion over what exactly a database is. This is especially true because different people use the same database terms to mean different things. Therefore, a good place to start our study is with a list and explanation of the most important database terms.

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Reviewing Basic Concepts What follows is a very brief overview of some basic database concepts. It is intended to either jolt your memory if you already have some database experience, or to provide you with the absolute basics, if you are new to databases. Understanding databases is an important part of mastering SQL, and you might want to find a good book on database fundamentals to brush up on the subject if needed.

What Is a Database? The term database is used in many different ways, but for our purposes (and indeed, from SQL’s perspective) a database is a collection of data stored in some organized fashion. The simplest way to think of it is to imagine a database as a filing cabinet. The filing cabinet is simply a physical location to store data, regardless of what that data is or how it is organized. Database A container (usually a file or set of files) to store organized data.

Misuse Causes Confusion People often use the term database to refer to the database software they are running. This is incorrect, and it is a source of much confusion. Database software is actually called the Database Management System (or DBMS). The database is the container created and manipulated via the DBMS. A database might be a file stored on a hard drive, but it might not. And for the most part this is not even significant as you never access a database directly anyway; you always use the DBMS and it accesses the database for you.

Understanding SQL

Tables When you store information in your filing cabinet you don’t just toss it in a drawer. Rather, you create files within the filing cabinet, and then you file related data in specific files. In the database world, that file is called a table. A table is a structured file that can store data of a specific type. A table might contain a list of customers, a product catalog, or any other list of information. Table

A structured list of data of a specific type.

The key here is that the data stored in the table is one type of data or one list. You would never store a list of customers and a list of orders in the same database table. Doing so would make subsequent retrieval and access difficult. Rather, you’d create two tables, one for each list. Every table in a database has a name that identifies it. That name is always unique—meaning no other table in that database can have the same name.

Table Names What makes a table name unique is actually a combination of several things including the database name and table name. Some databases also use the name of the database owner as part of the unique name. This means that while you cannot use the same table name twice in the same database, you definitely can reuse table names in different databases.

Tables have characteristics and properties that define how data is stored in them. These include information about what data may be stored, how it is broken up, how individual pieces of information are named, and much more. This set of information that describes a table is known as a schema, and schema are used to describe specific tables within a database, as well as entire databases (and the relationship between tables in them, if any). Schema Information about database and table layout and properties.

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Columns and Datatypes Tables are made up of columns. A column contains a particular piece of information within a table. Column A single field in a table. All tables are made up of one or more columns. The best way to understand this is to envision database tables as grids, somewhat like spreadsheets. Each column in the grid contains a particular piece of information. In a customer table, for example, one column contains the customer number, another contains the customer name, and the address, city, state, and zip are all stored in their own columns.

Breaking Up Data It is extremely important to break data into multiple columns correctly. For example, city, state, and zip should always be separate columns. By breaking these out, it becomes possible to sort or filter data by specific columns (for example, to find all customers in a particular state or in a particular city). If city and state are combined into one column, it would be extremely difficult to sort or filter by state.

Each column in a database has an associated datatype. A datatype defines what type of data the column can contain. For example, if the column is to contain a number (perhaps the number of items in an order), the datatype would be a numeric datatype. If the column were to contain dates, text, notes, currency amounts, and so on, the appropriate datatype would be used to specify this. Datatype A type of allowed data. Every table column has an associated datatype that restricts (or allows) specific data in that column. Datatypes restrict the type of data that can be stored in a column (for example, preventing the entry of alphabetical characters into a numeric field). Datatypes also help sort data correctly, and play an important role

Understanding SQL

in optimizing disk usage. As such, special attention must be given to picking the right datatype when tables are created.

Datatype Compatibility Datatypes and their names are one of the primary sources of SQL incompatibility. While most basic datatypes are supported consistently, many more advanced datatypes are not. And worse, occasionally you’ll find that the same datatype is referred to by different names in different DBMSs. There is not much you can do about this, but it is important to keep in mind when you create table schemas.

Rows Data in a table is stored in rows; each record saved is stored in its own row. Again, envisioning a table as a spreadsheet style grid, the vertical columns in the grid are the table columns, and the horizontal rows are the table rows. For example, a customers table might store one customer per row. The number of rows in the table is the number of records in it. Row

A record in a table.

Records or Rows? You may hear users refer to database records when referring to rows. For the most part, the two terms are used interchangeably, but row is technically the correct term.

Primary Keys Every row in a table should have some column (or set of columns) that uniquely identifies it. A table containing customers might use a customer

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Lesson 1

number column for this purpose, whereas a table containing orders might use the order ID. An employee list table might use an employee ID or the employee social security number column. Primary Key A column (or set of columns) whose values uniquely identify every row in a table. This column (or set of columns) that uniquely identifies each row in a table is called a primary key. The primary key is used to refer to a specific row. Without a primary key, updating or deleting specific rows in a table becomes extremely difficult as there is no guaranteed safe way to refer to just the rows to be affected.

Always Define Primary Keys Although primary keys are not actually required, most database designers ensure that every table they create has a primary key so that future data manipulation is possible and manageable.

Any column in a table can be established as the primary key, as long as it meets the following conditions: • No two rows can have the same primary key value. • Every row must have a primary key value (primary key columns may not allow NULL values). • Values in primary key columns can never be modified or updated. • Primary key values can never be reused. (If a row is deleted from the table, its primary key may not be assigned to any new rows in the future.)

Understanding SQL

Primary keys are usually defined on a single column within a table. But this is not required, and multiple columns may be used together as a primary key. When multiple columns are used, the rules listed above must apply to all columns that make up the primary key, and the values of all columns together must be unique (individual columns need not have unique values). There is another very important type of key called a foreign key, but I’ll get to that later on in Lesson 12, “Joining Tables.”

What Is SQL? SQL (pronounced as the letters S-Q-L or as sequel) is an abbreviation for Structured Query Language. SQL is a language designed specifically for communicating with databases. Unlike other languages (spoken languages like English, or programming languages like Java or Visual Basic), SQL is made up of very few words. This is deliberate. SQL is designed to do one thing and do it well—provide you with a simple and efficient way to read and write data from a database. What are the advantages of SQL? • SQL is not a proprietary language used by specific database vendors. Almost every major DBMS supports SQL, so learning this one language will enable you to interact with just about every database you’ll run into. • SQL is easy to learn. The statements are all made up of descriptive English words, and there aren’t that many of them. • Despite its apparent simplicity, SQL is actually a very powerful language, and by cleverly using its language elements you can perform very complex and sophisticated database operations. And with that, let’s learn SQL.

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Lesson 1

SQL Extensions Many DBMS vendors have extended their support for SQL by adding statements or instructions to the language. The purpose of these extensions is to provide additional functionality or simplified ways to perform specific operations. And while often extremely useful, these extensions tend to be very DBMS specific, and they are rarely supported by more than a single vendor. Standard SQL is governed by the ANSI standards committee, and is thus called ANSI SQL. All major DBMSs, even those with their own extensions, support ANSI SQL. Individual implementations have their own names (PL-SQL, Transact-SQL, and so forth). For the most part, the SQL taught in this book is ANSI SQL. On the odd occasion where DBMS specific SQL is used it is so noted.

Try It Yourself Like any language, the best way to learn SQL is to try it for yourself. To do this you’ll need a database and an application with which to test your SQL statements. All of the lessons in this book use real SQL statements and real database tables. Appendix A, “Sample Table Scripts,” explains what the example tables are, and provides details on how to obtain (or create) them so that you may follow along with the instructions in each lesson. Appendix B, “Working in Popular Applications,” describes the steps needed to execute your SQL in a variety of applications. Before proceeding to the next lesson, I’d strongly suggest that you turn to these two appendixes so that you’ll be ready to follow along.

Summary In this first lesson, you learned what SQL is and why it is useful. Because SQL is used to interact with databases, you also reviewed some basic database terminology.

LESSON 2

Retrieving Data In this lesson, you’ll learn how to use the SELECT statement to retrieve one or more columns of data from a table.

The SELECT Statement As explained in Lesson 1, “Understanding SQL,” SQL statements are made up of plain English terms. These terms are called keywords, and every SQL statement is made up of one or more keywords. The SQL statement that you’ll probably use most frequently is the SELECT statement. Its purpose is to retrieve information from one or more tables. Keyword A reserved word that is part of the SQL language. Never name a table or column using a keyword. Appendix E, “SQL Reserved Words,” lists some of the more common reserved words. To use SELECT to retrieve table data you must, at a minimum, specify two pieces of information—what you want to select, and from where you want to select it.

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Lesson 2

Following Along with the Examples The sample SQL statements (and sample output) throughout the lessons in this book use a set of data files that are described in Appendix A, “Sample Table Scripts.” If you’d like to follow along and try the examples yourself (I strongly recommend that you do so), refer to Appendix A which contains instructions on how to download or create these data files. It is important to understand that SQL is a language, not an application. The way that you specify SQL statements and display statement output varies from one application to the next. To assist you in adapting the examples to your own environment, Appendix B, “Working in Popular Applications,” explains how to issue the statements taught throughout this book using many popular applications and development environments. And if you need an application with which to follow along, Appendix B has recommendations for you too.

Retrieving Individual Columns We’ll start with a simple SQL SELECT statement, as follows:

INPUT

SELECT prod_name FROM Products;

The statement above uses the SELECT statement to retrieve a single column called prod_name from the Products table. The desired column name is specified right after the SELECT keyword, and the FROM keyword specifies the name of the table from which to retrieve the data. The output from this statement is shown in the following:

ANALYSIS

OUTPUT

prod_name -------------------Fish bean bag toy Bird bean bag toy Rabbit bean bag toy 8 inch teddy bear

Retrieving Data

12 inch teddy bear 18 inch teddy bear Raggedy Ann King doll Queen doll

Unsorted Data If you tried this query yourself you might have discovered that the data was displayed in a different order than shown here. If this is the case, don’t worry—it is working exactly as it is supposed to. If query results are not explicitly sorted (we’ll get to that in the next lesson) then data will be returned in no order of any significance. It may be the order in which the data was added to the table, but it may not. As long as your query returned the same number of rows then it is working.

A simple SELECT statement like the one used above returns all the rows in a table. Data is not filtered (so as to retrieve a subset of the results), nor is it sorted. We’ll discuss these topics in the next few lessons.

Use of White Space All extra white space within a SQL statement is ignored when that statement is processed. SQL statements can be specified on one long line or broken up over many lines. Most SQL developers find that breaking up statements over multiple lines makes them easier to read and debug.

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Lesson 2

Terminating Statements Multiple SQL statements must be separated by semicolons (the ; character). Most DBMSs do not require that a semicolon be specified after single statements. But if your particular DBMS complains, you might have to add it there. Of course, you can always add a semicolon if you wish. It’ll do no harm, even if it is, in fact, not needed. The exception to this rule is Sybase Adaptive Server which does not like SQL statements ending with ;. SQL Statement and Case It is important to note that SQL statements are case-insensitive, so SELECT is the same as select, which is the same as Select. Many SQL developers find that using uppercase for all SQL keywords and lowercase for column and table names makes code easier to read and debug. However, be aware that while the SQL language is case-insensitive, the names of tables, columns, and values may not be (that depends on your DBMS and how it is configured).

Retrieving Multiple Columns To retrieve multiple columns from a table, the same SELECT statement is used. The only difference is that multiple column names must be specified after the SELECT keyword, and each column must be separated by a comma.

Take Care with Commas When selecting multiple columns be sure to specify a comma between each column name, but not after the last column name. Doing so will generate an error.

Retrieving Data

The following SELECT statement retrieves three columns from the products table:

INPUT

SELECT prod_id, prod_name, prod_price FROM Products;

Just as in the prior example, this statement uses the SELECT statement to retrieve data from the Products table. In this example, three column names are specified, each separated by a comma. The output from this statement is shown below:

ANALYSIS

OUTPUT

prod_id --------BNBG01 BNBG02 BNBG03 BR01 BR02 BR03 RGAN01 RYL01 RYL02

prod_name -------------------Fish bean bag toy Bird bean bag toy Rabbit bean bag toy 8 inch teddy bear 12 inch teddy bear 18 inch teddy bear Raggedy Ann King doll Queen dool

prod_price ---------3.4900 3.4900 3.4900 5.9900 8.9900 11.9900 4.9900 9.4900 9.4900

Presentation of Data As you will notice in the above output, SQL statements typically return raw, unformatted data. Data formatting is a presentation issue, not a retrieval issue. Therefore, presentation (for example, displaying the above price values as currency amounts with the correct number of decimal places) is typically specified in the application that displays the data. Actual retrieved data (without applicationprovided formatting) is rarely used.

Retrieving All Columns In addition to being able to specify desired columns (one or more, as seen above), SELECT statements can also request all columns without having to list them individually. This is done using the asterisk (*) wildcard character in lieu of actual column names, as follows:

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Lesson 2

INPUT

SELECT * FROM Products;

When a wildcard (*) is specified, all the columns in the table are returned. The column order will typically, but not always, be the physical order in which the columns appear in the table definition. However, SQL data is seldom displayed as is. (Usually, it is returned to an application that formats or presents the data as needed.) As such, this should not pose a problem.

ANALYSIS

Using Wildcards As a rule, you are better off not using the * wildcard unless you really do need every column in the table. Even though use of wildcards may save you the time and effort needed to list the desired columns explicitly, retrieving unnecessary columns usually slows down the performance of your retrieval and your application.

Retrieving Unknown Columns There is one big advantage to using wildcards. As you do not explicitly specify column names (because the asterisk retrieves every column), it is possible to retrieve columns whose names are unknown.

Summary In this lesson, you learned how to use the SQL SELECT statement to retrieve a single table column, multiple table columns, and all table columns. Next you’ll learn how to sort the retrieved data.

LESSON 3

Sorting Retrieved Data In this lesson, you will learn how to use the SELECT statement’s ORDER clause to sort retrieved data as needed.

BY

Sorting Data As you learned in the last lesson, the following SQL statement returns a single column from a database table. But look at the output. The data appears to be displayed in no particular order at all.

INPUT OUTPUT

SELECT prod_name FROM Products; prod_name -------------------Fish bean bag toy Bird bean bag toy Rabbit bean bag toy 8 inch teddy bear 12 inch teddy bear 18 inch teddy bear Raggedy Ann King doll Queen doll

Actually, the retrieved data is not displayed in a mere random order. If unsorted, data will typically be displayed in the order in which it appears in the underlying tables. This could be the order in which the data was added to the tables initially. However, if data was subsequently updated or deleted, the order will be affected by how the DBMS reuses reclaimed

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Lesson 3

storage space. The end result is that you cannot (and should not) rely on the sort order if you do not explicitly control it. Relational database design theory states that the sequence of retrieved data cannot be assumed to have significance if ordering was not explicitly specified. Clause SQL statements are made up of clauses, some required and some optional. A clause usually consists of a keyword and supplied data. An example of this is the SELECT statement’s FROM clause, which you saw in the last lesson. To explicitly sort data retrieved using a SELECT statement, the ORDER clause is used. ORDER BY takes the name of one or more columns by which to sort the output. Look at the following example:

INPUT

BY

SELECT prod_name FROM Products ORDER BY prod_name;

This statement is identical to the earlier statement, except it also specifies an ORDER BY clause instructing the Database Management System software to sort the data alphabetically by the prod_name column. The results are as follows:

ANALYSIS

OUTPUT

prod_name -------------------12 inch teddy bear 18 inch teddy bear 8 inch teddy bear Bird bean bag toy Fish bean bag toy King doll Queen doll Rabbit bean bag toy Raggedy Ann

Position of ORDER BY Clause When specifying an ORDER BY clause, be sure that it is the last clause in your SELECT statement. Using clauses out of order will generate an error message.

Sorting Retrieved Data

Sorting by Nonselected Columns More often than not, the columns used in an ORDER BY clause will be ones that were selected for display. However, this is actually not required, and it is perfectly legal to sort data by a column that is not retrieved.

Sorting by Multiple Columns It is often necessary to sort data by more than one column. For example, if you are displaying an employee list, you might want to display it sorted by last name and first name (first by last name, and then within each last name sort by first name). This would be useful if there are multiple employees with the same last name. To sort by multiple columns, simply specify the column names separated by commas (just as you do when you are selecting multiple columns). The following code retrieves three columns and sorts the results by two of them—first by price and then by name.

INPUT OUTPUT

SELECT prod_id, prod_price, prod_name FROM Products ORDER BY prod_price, prod_name; prod_id ------BNBG02 BNBG01 BNBG03 RGAN01 BR01 BR02 RYL01 RYL02 BR03

prod_price ---------3.4900 3.4900 3.4900 4.9900 5.9900 8.9900 9.4900 9.4900 11.9900

prod_name -------------------Bird bean bag toy Fish bean bag toy Rabbit bean bag toy Raggedy Ann 8 inch teddy bear 12 inch teddy bear King doll Queen doll 18 inch teddy bear

It is important to understand that when you are sorting by multiple columns, the sort sequence is exactly as specified. In other words, using the output in the example above, the products are sorted by the prod_name column only when multiple rows have the same prod_price value. If all

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Lesson 3

the values in the prod_price column had been unique, no data would have been sorted by prod_name.

Sorting by Column Position In addition to being able to specify sort order using column names, ORDER BY also supports ordering specified by relative column position. The best way to understand this is to look at an example:

INPUT OUTPUT

SELECT prod_id, prod_price, prod_name FROM Products ORDER BY 2, 3; prod_id ------BNBG02 BNBG01 BNBG03 RGAN01 BR01 BR02 RYL01 RYL02 BR03

prod_price ---------3.4900 3.4900 3.4900 4.9900 5.9900 8.9900 9.4900 9.4900 11.9900

prod_name -------------------Bird bean bag toy Fish bean bag toy Rabbit bean bag toy Raggedy Ann 8 inch teddy bear 12 inch teddy bear King doll Queen doll 18 inch teddy bear

As you can see, the output is identical to that of the query above. The difference here is in the ORDER BY clause. Instead of specifying column names, the relative positions of selected columns in the SELECT list are specified. ORDER BY 2 means sort by the second column in the SELECT list, the prod_price column. ORDER BY 2, 3 means sort by prod_price and then by prod_name.

ANALYSIS

The primary advantage of this technique is that it saves retyping the column names. But there are some downsides too. First, not explicitly listing column names increases the likelihood of you mistakenly specifying the wrong column. Second, it is all too easy to mistakenly reorder data when making changes to the SELECT list (forgetting to make the corresponding changes to the ORDER BY clause). And finally, obviously you cannot use this technique when sorting by columns that are not in the SELECT list.

Sorting Retrieved Data

Sorting by Nonselected Columns Obviously, this technique cannot be used when sorting by columns that do not appear in the SELECT list. However, you can mix and match actual column names and relative column positions in a single statement if needed.

Specifying Sort Direction Data sorting is not limited to ascending sort orders (from A to Z). Although this is the default sort order, the ORDER BY clause can also be used to sort in descending order (from Z to A). To sort by descending order, the keyword DESC must be specified. The following example sorts the products by price in descending order (most expensive first):

INPUT OUTPUT

SELECT prod_id, prod_price, prod_name FROM Products ORDER BY prod_price DESC; prod_id ------BR03 RYL01 RYL02 BR02 BR01 RGAN01 BNBG01 BNBG02 BNBG03

prod_price ---------11.9900 9.4900 9.4900 8.9900 5.9900 4.9900 3.4900 3.4900 3.4900

prod_name -------------------18 inch teddy bear King doll Queen doll 12 inch teddy bear 8 inch teddy bear Raggedy Ann Fish bean bag toy Bird bean bag toy Rabbit bean bag toy

But what if you were to sort by multiple columns? The following example sorts the products in descending order (most expensive first), plus product name:

INPUT

SELECT prod_id, prod_price, prod_name FROM Products ORDER BY prod_price DESC, prod_name;

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Lesson 3

OUTPUT

prod_id ------BR03 RYL01 RYL02 BR02 BR01 RGAN01 BNBG02 BNBG01 BNBG03

prod_price ---------11.9900 9.4900 9.4900 8.9900 5.9900 4.9900 3.4900 3.4900 3.4900

prod_name -------------------18 inch teddy bear King doll Queen doll 12 inch teddy bear 8 inch teddy bear Raggedy Ann Bird bean bag toy Fish bean bag toy Rabbit bean bag toy

The DESC keyword only applies to the column name that directly precedes it. In the example above, DESC was specified for the prod_price column, but not for the prod_name column. Therefore, the prod_price column is sorted in descending order, but the prod_name column (within each price) is still sorted in standard ascending order.

ANALYSIS

Sorting Descending on Multiple Columns If you want to sort descending on multiple columns, be sure each column has its own DESC keyword.

It is worth noting that DESC is short for DESCENDING, and both keywords may be used. The opposite of DESC is ASC (or ASCENDING), which may be specified to sort in ascending order. In practice, however, ASC is not usually used because ascending order is the default sequence (and is assumed if neither ASC nor DESC are specified).

Sorting Retrieved Data

Case Sensitivity and Sort Orders When you are sorting textual data, is A the same as a? And does a come before B or after Z? These are not theoretical questions, and the answers depend on how the database is set up. In dictionary sort order, A is treated the same as a, and that is the default behavior for most Database Management Systems. However, most good DBMSs enable database administrators to change this behavior if needed. (If your database contains lots of foreign language characters, this might become necessary.) The key here is that if you do need an alternate sort order, you cannot accomplish it with a simple ORDER BY clause. You must contact your database administrator.

Summary In this lesson, you learned how to sort retrieved data using the SELECT statement’s ORDER BY clause. This clause, which must be the last in the SELECT statement, can be used to sort data on one or more columns as needed.

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LESSON 4

Filtering Data In this lesson, you will learn how to use the SELECT statement’s WHERE clause to specify search conditions.

Using the WHERE Clause Database tables usually contain large amounts of data, and you seldom need to retrieve all the rows in a table. More often than not you’ll want to extract a subset of the table’s data as needed for specific operations or reports. Retrieving just the data you want involves specifying search criteria, also known as a filter condition. Within a SELECT statement, data is filtered by specifying search criteria in the WHERE clause. The WHERE clause is specified right after the table name (the FROM clause) as follows:

INPUT

SELECT prod_name, prod_price FROM Products WHERE prod_price = 3.49;

This statement retrieves two columns from the products table, but instead of returning all rows, only rows with a prod_price value of 3.49 are returned, as follows:

ANALYSIS

OUTPUT

prod_name ------------------Fish bean bag toy Bird bean bag toy Rabbit bean bag toy

prod_price ---------3.4900 3.4900 3.4900

This example uses a simple equality test: It checks to see if a column has a specified value, and it filters the data accordingly. But SQL lets you do more than just test for equality.

Picky PostgreSQL PostgreSQL has very strict rules governing the values passed to SQL statements, especially pertaining to numbers used with decimal columns. As such, the previous example may not work as is on PostgreSQL. To get this example to work you may need to explicitly tell PostgreSQL that 3.49 is a valid number by including the type in the WHERE clause. To do this, replace = 3.49 with = decimal ‘3.49’.

SQL Versus Application Filtering Data can also be filtered at the application level. To do this, the SQL SELECT statement retrieves more data than is actually required for the client application, and the client code loops through the returned data to extract just the needed rows. As a rule, this practice is strongly discouraged. Databases are optimized to perform filtering quickly and efficiently. Making the client application (or development language) do the databases job will dramatically impact application performance and will create applications that cannot scale properly. In addition, if data is filtered at the client, the server has to send unneeded data across the network connections, resulting in a waste of network bandwidth usage.

WHERE Clause Position When using both ORDER BY and WHERE clauses, make sure that ORDER BY comes after the WHERE, otherwise an error will be generated.

(See Lesson 3, “Sorting Retrieved Data,” for more information on using ORDER BY.)

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Lesson 4

The WHERE Clause Operators The first WHERE clause we looked at tests for equality—determining if a column contains a specific value. SQL supports a whole range of conditional operators as listed in Table 4.1.

TABLE 4.1

WHERE Clause Operators

Operator

Description

=

Equality

Nonequality

!=

Nonequality


=

Greater than or equal to

!>

Not greater than

BETWEEN

Between two specified values

IS NULL

Is a NULL value

Operator Compatibility Some of the operators listed in Table 4.1 are redundant (for example, is the same as !=. !< (not less than) accomplishes the same effect as >= (greater than or equal to). Not all of these operators are supported by all DBMSs. Refer to your DBMS documentation to determine exactly what it supports.

Filtering Data

Checking against a Single Value We have already seen an example of testing for equality. Let’s take a look at a few examples to demonstrate the use of other operators. This first example lists all products that cost less than $10:

INPUT OUTPUT

SELECT prod_name, prod_price FROM Products WHERE prod_price < 10; prod_name ------------------Fish bean bag toy Bird bean bag toy Rabbit bean bag toy 8 inch teddy bear 12 inch teddy bear Raggedy Ann King doll Queen doll

prod_price ---------3.4900 3.4900 3.4900 5.9900 8.9900 4.9900 9.4900 9.4900

This next statement retrieves all products costing $10 or less (although the result will be the same as in the previous example because there are no items with a price of exactly $10):

INPUT

SELECT prod_name, prod_price FROM Products WHERE prod_price = 10; prod_name ------------------18 inch teddy bear

prod_price ---------11.9900

The only difference between this SELECT statement and the earlier one is that, in this statement, the first two WHERE clause conditions are enclosed within parentheses. As parentheses have a higher order of evaluation than either AND or OR operators, the DBMS first filters the OR condition within those parentheses. The SQL statement then becomes any products made by either vendor DLL01 or vendor BRS01 costing $10 or greater, which is exactly what we want.

ANALYSIS

Using Parentheses in WHERE Clauses Whenever you write WHERE clauses that use both AND and OR operators, use parentheses to explicitly group operators. Don’t ever rely on the default evaluation order, even if it is exactly what you want. There is no downside to using parentheses, and you are always better off eliminating any ambiguity.

Using the IN Operator The IN operator is used to specify a range of conditions, any of which can be matched. IN takes a comma-delimited list of valid values, all enclosed within parentheses. The following example demonstrates this:

INPUT

OUTPUT

SELECT prod_name, prod_price FROM Products WHERE vend_id IN (‘DLL01’,’BRS01’) ORDER BY prod_name; prod_name ------------------12 inch teddy bear 18 inch teddy bear

prod_price ---------8.9900 11.9900

Advanced Data Filtering

8 inch teddy bear Bird bean bag toy Fish bean bag toy Rabbit bean bag toy Raggedy Ann

5.9900 3.4900 3.4900 3.4900 4.9900

The SELECT statement retrieves all products made by vendor DLL01 and vendor BRS01. The IN operator is followed by a comma-delimited list of valid values, and the entire list must be enclosed within parentheses.

ANALYSIS

If you are thinking that the IN operator accomplishes the same goal as OR, you are right. The following SQL statement accomplishes the exact same thing as the example above:

INPUT

OUTPUT

SELECT prod_name, prod_price FROM Products WHERE vend_id = ‘DLL01’ OR vend_id = ‘BRS01’ ORDER BY prod_name; prod_name ------------------12 inch teddy bear 18 inch teddy bear 8 inch teddy bear Bird bean bag toy Fish bean bag toy Rabbit bean bag toy Raggedy Ann

prod_price ---------8.9900 11.9900 5.9900 3.4900 3.4900 3.4900 4.9900

Why use the IN operator? The advantages are • When you are working with long lists of valid options, the IN operator syntax is far cleaner and easier to read. • The order of evaluation is easier to manage when IN is used (as there will be fewer operators used). •

operators almost always execute more quickly than lists of OR operators. IN

• The biggest advantage of IN is that the IN operator can contain another SELECT statement, enabling you to build highly dynamic WHERE clauses. You’ll look at this in detail in Lesson 11, “Working with Subqueries.”

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Lesson 5

A keyword used in a WHERE clause to specify a list of values to be matched using an OR comparison.

IN

Using the NOT Operator The WHERE clause’s NOT operator has one function and one function only— NOT negates whatever condition comes next. Because NOT is never used by itself (it is always used in conjunction with some other operator), its syntax is a little different from all other operators. Unlike other operators, NOT can be used before the column to filter on, not just after it. NOT

A keyword used in a WHERE clause to negate a condition.

The following example demonstrates the use of NOT. To list the products made by all vendors except vendor DLL01, you can write the following:

INPUT

OUTPUT

SELECT prod_name FROM Products WHERE NOT vend_id = ‘DLL01’ ORDER BY prod_name; prod_name -----------------12 inch teddy bear 18 inch teddy bear 8 inch teddy bear King doll Queen doll

The NOT here negates the condition that follows it; so instead

ANALYSIS of matching vend_id to DLL01, the DBMS matches vend_id to anything that is not DLL01. The preceding example could have also been accomplished using the operator, as follows:

INPUT

SELECT prod_name FROM Products WHERE vend_id ‘DLL01’ ORDER BY prod_name;

Advanced Data Filtering

OUTPUT

prod_name -----------------12 inch teddy bear 18 inch teddy bear 8 inch teddy bear King doll Queen doll

Why use NOT? Well, for simple WHERE clauses such as the ones shown here, there really is no advantage to using NOT. NOT is useful in more complex clauses. For example, using NOT in conjunction with an IN operator makes it simple to find all rows that do not match a list of criteria.

ANALYSIS

NOT in MySQL

The form of NOT described here is not supported by MySQL. In MySQL NOT is only used to negate EXISTS (as in NOT EXISTS).

Summary This lesson picked up where the last lesson left off and taught you how to combine WHERE clauses with the AND and OR operators. You also learned how to explicitly manage the order of evaluation and how to use the IN and NOT operators.

39

LESSON 6

Using Wildcard Filtering In this lesson, you’ll learn what wildcards are, how they are used, and how to perform wildcard searches using the LIKE operator for sophisticated filtering of retrieved data.

Using the LIKE Operator All the previous operators we studied filter against known values. Be it matching one or more values, testing for greater-than or less-than known values, or checking a range of values, the common denominator is that the values used in the filtering are known. But filtering data that way does not always work. For example, how could you search for all products that contained the text bean bag within the product name? That cannot be done with simple comparison operators; that’s a job for wildcard searching. Using wildcards, you can create search patterns that can be compared against your data. In this example, if you want to find all products that contain the words bean bag, you can construct a wildcard search pattern enabling you to find that bean bag text anywhere within a product name. Wildcards Special characters used to match parts of a value. Search pattern A search condition made up of literal text, wildcard characters, or any combination of the two. The wildcards themselves are actually characters that have special meanings within SQL WHERE clauses, and SQL supports several wildcard types.

To use wildcards in search clauses, the LIKE operator must be used. LIKE instructs the DBMS that the following search pattern is to be compared using a wildcard match rather than a straight equality match.

Predicates When is an operator not an operator? When it is a predicate. Technically, LIKE is a predicate, not an operator. The end result is the same; just be aware of this term in case you run across it in SQL documentation or manuals.

Wildcard searching can be used only with text fields (strings); you can’t use wildcards to search fields of nontext datatypes.

The Percent Sign (%) Wildcard The most frequently used wildcard is the percent sign (%). Within a search string, % means match any number of occurrences of any character. For example, to find all products that start with the word Fish, you can issue the following SELECT statement:

INPUT OUTPUT

SELECT prod_id, prod_name FROM Products WHERE prod_name LIKE ‘Fish%’; prod_id ------BNBG01

prod_name -----------------Fish bean bag toy

This example uses a search pattern of ‘Fish%’. When this clause is evaluated, any value that starts with Fish is retrieved. The % tells the DBMS to accept any characters after the word Fish, regardless of how many characters there are.

ANALYSIS

Microsoft Access Wildcards If you are using Microsoft Access, you might need to use * instead of %.

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Lesson 6

Case-Sensitivity Depending on your DBMS and how it is configured, searches might be case-sensitive, in which case ‘fish%’ would not match Fish bean bag toy.

Wildcards can be used anywhere within the search pattern, and multiple wildcards can be used as well. The following example uses two wildcards, one at either end of the pattern:

INPUT OUTPUT

SELECT prod_id, prod_name FROM Products WHERE prod_name LIKE ‘%bean bag%’; prod_id -------BNBG01 BNBG02 BNBG03

prod_name -------------------Fish bean bag toy Bird bean bag toy Rabbit bean bag toy

The search pattern ‘%bean bag%’ means match any value that contains the text bean bag anywhere within it, regardless of any characters before or after that text.

ANALYSIS

Wildcards can also be used in the middle of a search pattern, although that is rarely useful. The following example finds all products that begin with an F and end with a y:

INPUT

SELECT prod_name FROM Products WHERE prod_name LIKE ‘F%y’;

It is important to note that, in addition to matching one or more characters, % also matches zero characters. % represents zero, one, or more characters at the specified location in the search pattern.

Using Wildcard Filtering

Watch for Trailing Spaces Many DBMSs, including Microsoft Access, pad field contents with spaces. For example, if a column expects 50 characters and the text stored is Fish bean bag toy (17 characters), 33 spaces might be appended to the text to fully fill the column. This usually has no real impact on data and how it is used, but it could negatively affect the previous SQL statement. The clause WHERE prod_name LIKE ‘F%y’ matches only prod_name if it starts with F and ends with y. If the value is padded with spaces, it does not end with y, so Fish bean bag toy is not retrieved. One simple solution to this problem is to append a second % to the search pattern: ‘F%y%’ also matches characters (or spaces) after the y. A better solution is to trim the spaces using functions, as is discussed in Lesson 8, “Using Data Manipulation Functions.”

The Underscore (_) Wildcard Another useful wildcard is the underscore (_). The underscore is used just like %, but instead of matching multiple characters, the underscore matches just a single character.

Microsoft Access Wildcards If you are using Microsoft Access, you might need to use ? instead of _.

Take a look at this example:

INPUT

SELECT prod_id, prod_name FROM Products WHERE prod_name LIKE ‘__ inch teddy bear’;

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Lesson 6

Watch for Trailing Spaces As in the previous example, you might have to append a wildcard to the pattern for this example to work.

OUTPUT

prod_id -------BR02 BR03

prod_name -------------------12 inch teddy bear 18 inch teddy bear

The search pattern used in this WHERE clause specifies two wildcards followed by literal text. The results shown are the only rows that match the search pattern: The underscore matches 12 in the first row and 18 in the second row. The 8 inch teddy bear product did not match because the search pattern requires two wildcard matches, not one. By contrast, the following SELECT statement uses the % wildcard and returns three matching products:

ANALYSIS

INPUT OUTPUT

SELECT prod_id, prod_name FROM Products WHERE prod_name LIKE ‘% inch teddy bear’; prod_id -------BR01 BR02 BR03

prod_name -------------------8 inch teddy bear 12 inch teddy bear 18 inch teddy bear

Unlike %, which can match zero characters, _ always matches one character—no more and no less.

The Brackets ([]) Wildcard The brackets ([]) wildcard is used to specify a set of characters, any one of which must match a character in the specified position (the location of the wildcard).

Using Wildcard Filtering

Sets Are Not Always Supported Unlike the wildcards described thus far, the use of [] to create sets is not supported by all DBMSs. Sets are supported by Microsoft Access, Microsoft SQL Server, and Sybase Adaptive Server. Consult your DBMS documentation to determine whether sets are supported.

For example, to find all contacts whose names begin with the letter J or the letter M, you can do the following:

INPUT

OUTPUT

SELECT cust_contact FROM Customers WHERE cust_contact LIKE ‘[JM]%’ ORDER BY cust_contact; cust_contact ----------------Jim Jones John Smith Michelle Green

The WHERE clause in this statement is ‘[JM]%’. This search pattern uses two different wildcards. The [JM] matches any contact name that begins with either of the letters within the brackets, and it also matches only a single character. Therefore, any names longer than one character do not match. The % wildcard after the [JM] matches any number of characters after the first character, returning the desired results.

ANALYSIS

This wildcard can be negated by prefixing the characters with ^ (the carat character). For example, the following matches any contact name that does not begin with the letter J or the letter M (the opposite of the previous example):

INPUT

SELECT cust_contact FROM Customers WHERE cust_contact LIKE ‘[^JM]%’ ORDER BY cust_contact;

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Lesson 6

Negating Sets in Microsoft Access If you are using Microsoft Access, you might need to use ! instead of ^ to negate a set—so use [!JM] instead of [^JM].

Of course, you can accomplish the same result using the NOT operator. The only advantage of ^ is that it can simplify the syntax if you are using multiple WHERE clauses:

INPUT

SELECT cust_contact FROM Customers WHERE NOT cust_contact LIKE ‘[JM]%’ ORDER BY cust_contact;

Caution The brackets ([]) wildcard is not supported by all DBMSs. Consult your DBMS documentation to find out whether this particular wildcard is supported.

Tips for Using Wildcards As you can see, SQL’s wildcards are extremely powerful. But that power comes with a price: Wildcard searches typically take far longer to process than any other search types discussed previously. Here are some tips to keep in mind when using wildcards: • Don’t overuse wildcards. If another search operator will do, use it instead. • When you do use wildcards, try to not use them at the beginning of the search pattern unless absolutely necessary. Search patterns that begin with wildcards are the slowest to process. • Pay careful attention to the placement of the wildcard symbols. If they are misplaced, you might not return the data you intended. Having said that, wildcards are an important and useful search tool, and one that you will use frequently.

Using Wildcard Filtering

Summary In this lesson, you learned what wildcards are and how to use SQL wildcards within your WHERE clauses. You also learned that wildcards should be used carefully and never overused.

47

LESSON 7

Creating Calculated Fields In this lesson, you will learn what calculated fields are, how to create them, and how to use aliases to refer to them from within your application.

Understanding Calculated Fields Data stored within a database’s tables is often not available in the exact format needed by your applications. Here are some examples: • You need to display a field containing the name of a company along with the company’s location, but that information is stored in separated table columns. • City, state, and ZIP Code are stored in separate columns (as they should be), but your mailing label printing program needs them retrieved as one correctly formatted field. • Column data is in mixed upper- and lowercase, and your report needs all data presented in uppercase. • An Order Items table stores item price and quantity but not the expanded price (price multiplied by quantity) of each item. To print invoices, you need that expanded price. • You need total, averages, or other calculations based on table data. In each of these examples, the data stored in the table is not exactly what your application needs. Rather than retrieve the data as it is and then

reformat it within your client application or report, what you really want is to retrieve converted, calculated, or reformatted data directly from the database. This is where calculated fields come in. Unlike all the columns we retrieved in the lessons thus far, calculated fields don’t actually exist in database tables. Rather, a calculated field is created on-the-fly within a SQL SELECT statement. Field Essentially means the same thing as column and often is used interchangeably, although database columns are typically called columns and the term fields is normally used in conjunction with calculated fields. It is important to note that only the database knows which columns in a SELECT statement are actual table columns and which are calculated fields. From the perspective of a client (for example, your application), a calculated field’s data is returned in the same way as data from any other column.

Client Versus Server Formatting Many of the conversions and reformatting that can be performed within SQL statements can also be performed directly in your client application. However, as a rule, it is far quicker to perform these operations on the database server than it is to perform them within the client because DBMSs are built to perform this type of processing quickly and efficiently.

Concatenating Fields To demonstrate working with calculated fields, let’s start with a simple example—creating a title made up of two columns. The Vendors table contains vendor name and address information. Imagine that you are generating a vendor report and need to list the vendor location as part of the vendor name in the format name (location).

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Lesson 7

The report wants a single value, and the data in the table is stored in two columns: vend_name and vend_country. In addition, you need to surround vend_country with parenthesis, and those are definitely not stored in the database table. The SELECT statement that returns the vendor names and locations is simple enough, but how would you create this combined value? Concatenate Joining values together (by appending them to each other) to form a single long value. The solution is to concatenate the two columns. In SQL SELECT statements, you can concatenate columns using a special operator. Depending on which DBMS you are using, this can be a plus sign (+) or two pipes (||).

+ or ||? Access, SQL Server, and Sybase support + for concatenation. DB2, Oracle, PostgreSQL, and Sybase support ||. Refer to your DBMS documentation for more details. || is actually the preferred syntax, so more and more DBMSs are implementing support for it.

Here’s an example using the plus sign (the syntax used by most DBMSs):

INPUT OUTPUT

SELECT vend_name + ‘ (‘ + vend_country + ‘)’ FROM Vendors ORDER BY vend_name; ---------------------------------------------------Bear Emporium (USA ) Bears R Us (USA ) Doll House Inc. (USA ) Fun and Games (England ) Furball Inc. (USA ) Jouets et ours (France )

The following is the same statement, but using the || syntax:

INPUT

SELECT vend_name || ‘ (‘ || vend_country || ‘)’ FROM Vendors ORDER BY vend_name;

Creating Calculated Fields

OUTPUT

ANALYSIS

---------------------------------------------------Bear Emporium (USA ) Bears R Us (USA ) Doll House Inc. (USA ) Fun and Games (England ) Furball Inc. (USA ) Jouets et ours (France )

The previous SELECT statements concatenate the following elements:

• The name stored in the vend_name column • A string containing a space and an open parenthesis • The country stored in the vend_country column • A string containing the close parenthesis As you can see in the output shown previously, the SELECT statement returns a single column (a calculated field) containing all four of these elements as one unit.

Concatenation in MySQL MySQL does not support concatenation using + or ||. Rather, it requires the use of a CONCAT() function that takes a list of items to be concatenated. Using CONCAT(), the first line of the example would be as follows: SELECT CONCAT(vend_name, ‘ (‘, vend_country, ‘)’)

MySQL does support the use of ||, but not for concatenation. In MySQL || is equivalent to the operator OR, and && is equivalent to the operator AND.

Look again at the output returned by the SELECT statement. The two columns incorporated into the calculated field are padded with spaces. Many databases (although not all) save text values padded to the column width. To return the data formatted properly, you must trim those padded spaces. This can be done using the SQL RTRIM() function, as follows:

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Lesson 7

INPUT

OUTPUT

SELECT RTRIM(vend_name) + ‘ (‘ + RTRIM(vend_country) ➥+ ‘)’ FROM Vendors ORDER BY vend_name; ---------------------------------------------------Bear Emporium (USA) Bears R Us (USA) Doll House Inc. (USA) Fun and Games (England) Furball Inc. (USA) Jouets et ours (France)

The following is the same statement, but using the || syntax:

INPUT

OUTPUT

SELECT RTRIM(vend_name) || ‘ (‘ || ➥RTRIM(vend_country) || ‘)’ FROM Vendors ORDER BY vend_name; ---------------------------------------------------Bear Emporium (USA) Bears R Us (USA) Doll House Inc. (USA) Fun and Games (England) Furball Inc. (USA) Jouets et ours (France)

The RTRIM() function trims all space from the right of a

ANALYSIS value.

The TRIM Functions Most DBMSs support RTRIM() (which, as just seen, trims the right side of a string), as well as LTRIM() (which trims the left side of a string), and TRIM() (which trims both the right and left).

Creating Calculated Fields

Using Aliases The SELECT statement used to concatenate the address field works well, as seen in the previous output. But what is the name of this new calculated column? Well, the truth is, it has no name; it is simply a value. Although this can be fine if you are just looking at the results in a SQL query tool, an unnamed column cannot be used within a client application because the client has no way to refer to that column. To solve this problem, SQL supports column aliases. An alias is just that, an alternative name for a field or value. Aliases are assigned with the AS keyword. Take a look at the following SELECT statement:

INPUT

OUTPUT

SELECT RTRIM(vend_name) + ‘ (‘ + RTRIM(vend_country) ➥+ ‘)’ AS vend_title FROM Vendors ORDER BY vend_name; vend_title ---------------------------------------------------Bear Emporium (USA) Bears R Us (USA) Doll House Inc. (USA) Fun and Games (England) Furball Inc. (USA) Jouets et ours (France)

The following is the same statement, but using the || syntax:

INPUT

OUTPUT

SELECT RTRIM(vend_name) || ‘ (‘ || ➥RTRIM(vend_country) || ‘)’ AS vend_title FROM Vendors ORDER BY vend_name; vend_title ---------------------------------------------------Bear Emporium (USA) Bears R Us (USA) Doll House Inc. (USA) Fun and Games (England) Furball Inc. (USA) Jouets et ours (France)

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The SELECT statement itself is the same as the one used in the previous code snippet, except that here the calculated field is followed by the text AS vend_title. This instructs SQL to create a calculated field named vend_title containing the calculation specified. As you can see in the output, the results are the same as before, but the column is now named vend_title and any client application can refer to this column by name, just as it would to any actual table column.

ANALYSIS

Other Uses for Aliases Aliases have other uses, too. Some common uses include renaming a column if the real table column name contains illegal characters (for example, spaces) and expanding column names if the original names are either ambiguous or easily misread.

Alias Names Aliases can be single words or complete strings. If the latter is used, the string should be enclosed within quotes. This practice is legal but is strongly discouraged. Although multiword names are indeed highly readable, they create all sorts of problems for many client applications. So much so that one of the most common uses of aliases is to rename multiword column names to single-word names (as explained previously).

Derived Columns Aliases are also sometimes referred to as derived columns, so regardless of the term you run across, they mean the same thing.

Creating Calculated Fields

Performing Mathematical Calculations Another frequent use for calculated fields is performing mathematical calculations on retrieved data. Let’s take a look at an example. The Orders table contains all orders received, and the OrderItems table contains the individual items within each order. The following SQL statement retrieves all the items in order number 20008:

INPUT OUTPUT

SELECT prod_id, quantity, item_price FROM OrderItems WHERE order_num = 20008; prod_id ---------RGAN01 BR03 BNBG01 BNBG02 BNBG03

quantity ----------5 5 10 10 10

item_price --------------------4.9900 11.9900 3.4900 3.4900 3.4900

The item_price column contains the per unit price for each item in an order. To expand the item price (item price multiplied by quantity ordered), you simply do the following:

INPUT

OUTPUT

SELECT prod_id, quantity, item_price, quantity*item_price AS expanded_price FROM OrderItems WHERE order_num = 20008; prod_id ------RGAN01 BR03 BNBG01 BNBG02 BNBG03

quantity -------5 5 10 10 10

item_price ---------4.9900 11.9900 3.4900 3.4900 3.4900

expanded_price -------------24.9500 59.9500 34.9000 34.9000 34.9000

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The expanded_price column shown in the previous output is

ANALYSIS a calculated field; the calculation is simply quantity*item_price.

The client application can now use this new calculated column just as it would any other column.

SQL supports the basic mathematical operators listed in Table 7.1. In addition, parentheses can be used to establish order of precedence. Refer to Lesson 5, “Advanced Data Filtering,” for an explanation of precedence.

TABLE 7.1

SQL Mathematical Operators

Operator

Description

+

Addition

-

Subtraction

*

Multiplication

/

Division

Summary In this lesson, you learned what calculated fields are and how to create them. We used examples demonstrating the use of calculated fields for both string concatenation and mathematical operations. In addition, you learned how to create and use aliases so your application can refer to calculated fields.

LESSON 8

Using Data Manipulation Functions In this lesson, you’ll learn what functions are, what types of functions DBMSs support, and how to use these functions. You’ll also learn why SQL function use can be very problematic.

Understanding Functions Like almost any other computer language, SQL supports the use of functions to manipulate data. Functions are operations that are usually performed on data, usually to facilitate conversion and manipulation. An example of a function is the RTRIM() that we used in the last lesson to trim any spaces from the end of a string.

The Problem with Functions Before you work through this lesson and try the examples, you should be aware that using SQL functions can be highly problematic. Unlike SQL statements (for example, SELECT), which for the most part are supported by all DBMSs equally, functions tend to be very DBMS specific. In fact, very few functions are supported identically by all major DBMSs. Although all types of functionality are usually available in each DBMS, the implementation of that functionality can differ greatly. To demonstrate just how problematic this can be, Table 8.1 lists three commonly needed functions and their syntax as employed by various DBMSs:

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TABLE 8.1

DBMS Function Differences

Function

Syntax

Extract part of a string

Access uses MID(). DB2, Oracle, and PostgreSQL use SUBSTR(). MySQL, SQL Server, and Sybase use SUBSTRING().

Datatype conversion

Access and Oracle use multiple functions, one for each conversion type. DB2 and PostgreSQL use CAST(). MySQL, SQL Server, and Sybase use CONVERT().

Get current date

Access uses NOW(). DB2 and PostgreSQL use CURRENT_DATE. MySQL uses CURDATE(). Oracle uses SYSDATE. SQL Server and Sybase use GETDATE().

As you can see, unlike SQL statements, SQL functions are not portable. This means that code you write for a specific SQL implementation might not work on another implementation. Portable Code that is written so that it will run on multiple systems. With code portability in mind, many SQL programmers opt not to use any implementation-specific features. Although this is a somewhat noble and idealistic view, it is not always in the best interests of application performance. If you opt not to use these functions, you make your application code work harder. It must use other methods to do what the DBMS could have done more efficiently.

Using Data Manipulation Functions

Should You Use Functions? So now you are trying to decide whether you should or shouldn’t use functions. Well, that decision is yours, and there is no right or wrong choice. If you do decide to use functions, make sure you comment your code well, so that at a later date you (or another developer) will know exactly what SQL implementation you were writing to.

Using Functions Most SQL implementations support the following types of functions: • Text functions are used to manipulate strings of text (for example, trimming or padding values and converting values to upper and lowercase). • Numeric functions are used to perform mathematical operations on numeric data (for example, returning absolute numbers and performing algebraic calculations). • Date and time functions are used to manipulate date and time values and to extract specific components from these values (for example, returning differences between dates, and checking date validity). • System functions return information specific to the DBMS being used (for example, returning user login information). In the last lesson, you saw a function used as part of a column list in a statement, but that’s not all functions can do. You can use functions in other parts of the SELECT statement (for instance in the WHERE clause), as well as in other SQL statements (more on that in later lessons). SELECT

Text Manipulation Functions You’ve already seen an example of text-manipulation functions in the last lesson—the RTRIM() function was used to trim white space from the end of a column value. Here is another example, this time using the UPPER() function:

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INPUT

OUTPUT

SELECT vend_name, UPPER(vend_name) AS vend_name_upcase FROM Vendors ORDER BY vend_name; vend_name ----------------------------Bear Emporium Bears R Us Doll House Inc. Fun and Games Furball Inc. Jouets et ours

vend_name_upcase ------------------BEAR EMPORIUM BEARS R US DOLL HOUSE INC. FUN AND GAMES FURBALL INC. JOUETS ET OURS

ANALYSIS As you can see, UPPER() converts text to upper-case and so

in this example each vendor is listed twice, first exactly as stored in the Vendors table, and then converted to upper case as column vend_name_upcase. Table 8.2 lists some commonly used text-manipulation functions.

TABLE 8.2

Commonly Used Text-Manipulation Functions

Function

Description

LEFT() (or use substring function)

Returns characters from left of string

LENGTH()

(also

DATALENGTH()

Returns the length of a string or LEN())

LOWER()

(LCASE() if using Access)

Converts string to lowercase

LTRIM()

Trims white space from left of string

RIGHT() (or use substring function)

Returns characters from right of string

RTRIM()

Trims white space from right of string

SOUNDEX()

Returns a string’s SOUNDEX value

UPPER() (UCASE()

using Access)

if

Converts string to uppercase

Using Data Manipulation Functions

One item in Table 8.2 requires further explanation. SOUNDEX is an algorithm that converts any string of text into an alphanumeric pattern describing the phonetic representation of that text. SOUNDEX takes into account similar sounding characters and syllables, enabling strings to be compared by how they sound rather than how they have been typed. Although SOUNDEX is not a SQL concept, most DBMSs do offer SOUNDEX support.

SOUNDEX Support

SOUNDEX() is not supported by Microsoft Access or PostgreSQL, and so the following example will not work on those DBMSs.

Here’s an example using the SOUNDEX() function. Customer Kids Place is in the Customers table and has a contact named Michelle Green. But what if that were a typo, and the contact actually was supposed to have been Michael Green? Obviously, searching by the correct contact name would return no data, as shown here:

INPUT OUTPUT

SELECT cust_name, cust_contact FROM Customers WHERE cust_contact = ‘Michael Green’; cust_name --------------------------

cust_contact ---------------------

Now try the same search using the SOUNDEX() function to match all contact names that sound similar to Michael Green:

INPUT

OUTPUT

SELECT cust_name, cust_contact FROM Customers WHERE SOUNDEX(cust_contact) = SOUNDEX(‘Michael ➥Green’); cust_name -------------------------Kids Place

cust_contact --------------------Michelle Green

In this example, the WHERE clause uses the SOUNDEX() func-

ANALYSIS tion to convert both the cust_contact column value and the search string to their SOUNDEX values. Because Michael

Green

and

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sound alike, their SOUNDEX values match, and so the clause correctly filtered the desired data.

Michelle Green WHERE

Date and Time Manipulation Functions Date and times are stored in tables using datatypes, and each DBMS uses its own special varieties. Date and time values are stored in special formats so that they may be sorted or filtered quickly and efficiently, as well as to save physical storage space. The format used to store dates and times is usually of no use to your applications, and so date and time functions are almost always used to read, expand, and manipulate these values. Because of this, date and time manipulation functions are some of the most important functions in the SQL language. Unfortunately, they also tend to be the least consistent and least portable. To demonstrate the use of date manipulation function, here is a simple example. The Orders table contains all orders along with an order date. To retrieve a list of all orders made in 2004 in SQL Server and Sybase, do the following:

INPUT OUTPUT

SELECT order_num FROM Orders WHERE DATEPART(yy, order_date) = 2004; order_num ----------20005 20006 20007 20008 20009

In Access use this version:

INPUT

SELECT order_num FROM Orders WHERE DATEPART(‘yyyy’, order_date) = 2004;

This example (both the SQL Server and Sybase version, and the Access version) uses the DATEPART() function which, as its name suggests, returns a part of a date. DATEPART() takes two parameters, the part to return, and the date to return it from. In our example

ANALYSIS

Using Data Manipulation Functions

returns just the year from the order_date column. By comparing that to 2004, the WHERE clause can filter just the orders for that year.

DATEPART()

Here is the PostgreSQL version that uses a similar function named DATE_PART():

INPUT

SELECT order_num FROM Orders WHERE DATE_PART(‘year’, order_date) = 2004;

MySQL has all sorts of date manipulation functions, but not DATEPART(). MySQL users can use a function named YEAR() to extract the year from a date:

INPUT

SELECT order_num FROM Orders WHERE YEAR(order_date) = 2004;

Oracle has no DATEPART() function either, but there are several other date manipulation functions that can be used to accomplish the same retrieval. Here is an example:

INPUT

SELECT order_num FROM Orders WHERE to_number(to_char(order_date, ‘YY’)) = 2004;

In this example, the to_char() function is used to extract part of the date, and to_number() is used to convert it to a numeric value so that it can be compared to 2004.

ANALYSIS

Another way to accomplish this same task is to use the BETWEEN operator:

INPUT

SELECT order_num FROM Orders WHERE order_date BETWEEN to_date(‘01-JAN-2004’) AND to_date(‘31-DEC-2004’);

In this example, Oracle’s to_date() function is used to convert two strings to dates. One contains the date January 1, 2004, and the other contains the date December 31, 2004. A standard BETWEEN operator is used to find all orders between those two dates. It is worth noting that this same code would not work with SQL Server because it does not support the to_date() function. However, if you replaced to_date() with DATEPART(), you could indeed use this type of statement.

ANALYSIS

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Oracle Dates Dates in the format of DD-MMM-YYYY (as in the example shown above) are usually processed by Oracle correctly even if not explicitly cast as dates using to_date(); however, to be safe, that function should always be used.

The example shown here extracted and used part of a date (the year). To filter by a specific month, the same process could be used, specifying an AND operator and both year and month comparisons. DBMSs typically offer far more than simple date part extraction. Most have functions for comparing dates, performing date based arithmetic, options for formatting dates, and more. But, as you have seen, date-time manipulation functions are particularly DBMS specific. Refer to your DBMS documentation for the list of the date-time manipulation functions it supports.

Numeric Manipulation Functions Numeric manipulation functions do just that—manipulate numeric data. These functions tend to be used primarily for algebraic, trigonometric, or geometric calculations and, therefore, are not as frequently used as string or date and time manipulation functions. The ironic thing is that of all the functions found in the major DBMSs, the numeric functions are the ones that are most uniform and consistent. Table 8.3 lists some of the more commonly used numeric manipulation functions.

TABLE 8.3 Functions

Commonly Used Numeric Manipulation

Function

Description

ABS()

Returns a number’s absolute value

COS()

Returns the trigonometric cosine of a specified angle

EXP()

Returns the exponential value of a specific number

Using Data Manipulation Functions

Function

Description

PI()

Returns the value of PI

SIN()

Returns the trigonometric sine of a specified angle

SQRT()

Returns the square root of a specified number

TAN()

Returns the trigonometric tangent of a specified angle

Refer to your DBMS documentation for a list of the supported mathematical manipulation functions.

Summary In this lesson, you learned how to use SQL’s data manipulation functions. You also learned that although these functions can be extremely useful in formatting, manipulating, and filtering data, the function details are very inconsistent from one SQL implementation to the next.

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LESSON 9

Summarizing Data In this lesson, you will learn what the SQL aggregate functions are and how to use them to summarize table data.

Using Aggregate Functions It is often necessary to summarize data without actually retrieving it all, and SQL provides special functions for this purpose. Using these functions, SQL queries are often used to retrieve data for analysis and reporting purposes. Examples of this type of retrieval are • Determining the number of rows in a table (or the number of rows that meet some condition or contain a specific value). • Obtaining the sum of a set of rows in a table. • Finding the highest, lowest, and average values in a table column (either for all rows or for specific rows). In each of these examples, you want a summary of the data in a table, not the actual data itself. Therefore, returning the actual table data would be a waste of time and processing resources (not to mention bandwidth). To repeat, all you really want is the summary information. To facilitate this type of retrieval, SQL features a set of five aggregate functions, which are listed in Table 9.1. These functions enable you to perform all the types of retrieval just enumerated. You’ll be relieved to know that unlike the data manipulation functions in the last lesson, SQL’s aggregate functions are supported pretty consistently by the major SQL implementations.

Aggregate Functions Functions that operate on a set of rows to calculate and return a single value.

TABLE 9.1

SQL Aggregate Functions

Function

Description

AVG()

Returns a column’s average value

COUNT()

Returns the number of rows in a column

MAX()

Returns a column’s highest value

MIN()

Returns a column’s lowest value

SUM()

Returns the sum of a column’s values

The use of each of these functions is explained in the following sections.

The AVG() Function AVG() is used to return the average value of a specific column by counting both the number of rows in the table and the sum of their values. AVG() can be used to return the average value of all columns or of specific columns or rows.

This first example uses AVG() to return the average price of all the products in the Products table:

INPUT OUTPUT

SELECT AVG(prod_price) AS avg_price FROM Products; avg_price ------------6.823333

The SELECT statement above returns a single value, avg_price that contains the average price of all products in the Products table. avg_price is an alias as explained in Lesson 7, “Creating Calculated Fields.”

ANALYSIS

AVG() can also be used to determine the average value of specific columns or rows. The following example returns the average price of products offered by a specific vendor:

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SELECT AVG(prod_price) AS avg_price FROM Products WHERE vend_id = ‘DLL01’;

INPUT

avg_price ----------3.8650

OUTPUT

This SELECT statement differs from the previous one only in that this one contains a WHERE clause. The WHERE clause filters only products with a vendor_id of DLL01, and, therefore, the value returned in avg_price is the average of just that vendor’s products.

ANALYSIS

Individual Columns Only AVG() may only be used to determine the average of a specific numeric column, and that column name must be specified as the function parameter. To obtain the average value of multiple columns, multiple AVG() functions must be used.

NULL Values Column rows containing NULL values are ignored by the AVG() function.

The COUNT() Function does just that: It counts. Using COUNT(), you can determine the number of rows in a table or the number of rows that match a specific criterion.

COUNT()

COUNT()

can be used two ways:

• Use COUNT(*) to count the number of rows in a table, whether columns contain values or NULL values. • Use COUNT(column) to count the number of rows that have values in a specific column, ignoring NULL values. This first example returns the total number of customers in the Customers table:

Summarizing Data

INPUT

SELECT COUNT(*) AS num_cust FROM Customers;

OUTPUT

num_cust -------5

ANALYSIS

In this example, COUNT(*) is used to count all rows, regardless of values. The count is returned in num_cust.

The following example counts just the customers with an email address:

INPUT OUTPUT

SELECT COUNT(cust_email) AS num_cust FROM Customers; num_cust -------3

This SELECT statement uses COUNT(cust_email) to count only rows with a value in the cust_email column. In this example, cust_email is 3 (meaning that only 3 of the 5 customers have email addresses).

ANALYSIS

NULL Values Column rows with NULL values in them are ignored by the COUNT() function if a column name is specified, but not if the asterisk (*) is used.

The MAX() Function returns the highest value in a specified column. MAX() requires that the column name be specified, as seen here:

MAX()

INPUT OUTPUT ANALYSIS

SELECT MAX(prod_price) AS max_price FROM Products; max_price ---------11.9900

Here MAX() returns the price of the most expensive item in Products table.

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Using MAX() with Non-Numeric Data Although MAX() is usually used to find the highest numeric or date values, many (but not all) DBMSs allow it to be used to return the highest value in any columns including textual columns. When used with textual data, MAX() returns the row that would be the last if the data were sorted by that column.

NULL Values Column rows with NULL values in them are ignored by the MAX() function.

The MIN() Function does the exact opposite of MAX(); it returns the lowest value in a specified column. Like MAX(), MIN() requires that the column name be specified, as seen here:

MIN()

INPUT OUTPUT ANALYSIS

SELECT MIN(prod_price) AS min_price FROM Products; min_price ---------3.4900

Here MIN() returns the price of the least expensive item in Products table.

Using MIN() with Non-Numeric Data Although MIN() is usually used to find the lowest numeric or date values, many (but not all) DBMSs allow it to be used to return the lowest value in any columns including textual columns. When used with textual data, MIN() will return the row that would be first if the data were sorted by that column.

Summarizing Data

NULL Values Column rows with NULL values in them are ignored by the MIN() function.

The SUM() Function SUM()

is used to return the sum (total) of the values in a specific column.

Here is an example to demonstrate this. The OrderItems table contains the actual items in an order, and each item has an associated quantity. The total number of items ordered (the sum of all the quantity values) can be retrieved as follows:

INPUT OUTPUT

SELECT SUM(quantity) AS items_ordered FROM OrderItems WHERE order_num = 20005; items_ordered ---------200

The function SUM(quantity) returns the sum of all the item quantities in an order, and the WHERE clause ensures that just the right order items are included.

ANALYSIS

can also be used to total calculated values. In this next example the total order amount is retrieved by totaling item_price*quantity for each item:

SUM()

INPUT OUTPUT

SELECT SUM(item_price*quantity) AS total_price FROM OrderItems WHERE order_num = 20005; total_price ---------1648.0000

The function SUM(item_price*quantity) returns the sum of all the expanded prices in an order, and again the WHERE clause ensures that just the right order items are included.

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Performing Calculations on Multiple Columns All the aggregate functions can be used to perform calculations on multiple columns using the standard mathematical operators, as shown in the example.

NULL Values Column rows with NULL values in them are ignored by the SUM() function.

Aggregates on Distinct Values The five aggregate functions can all be used in two ways: • To perform calculations on all rows, specify the ALL argument or specify no argument at all (because ALL is the default behavior). • To only include unique values, specify the DISTINCT argument.

ALL Is Default The ALL argument need not be specified because it is the default behavior. If DISTINCT is not specified, ALL is assumed.

Not in Access Microsoft Access does not support the use of DISTINCT within aggregate functions, and so the following example will not work with Access.

The following example uses the AVG() function to return the average product price offered by a specific vendor. It is the same SELECT statement used above, but here the DISTINCT argument is used so that the average only takes into account unique prices:

INPUT

SELECT AVG(DISTINCT prod_price) AS avg_price FROM Products WHERE vend_id = ‘DLL01’;

Summarizing Data

OUTPUT

avg_price ----------4.2400

As you can see, in this example avg_price is higher when DISTINCT is used because there are multiple items with the same lower price. Excluding them raises the average price.

ANALYSIS

Caution DISTINCT may only be used with COUNT() if a column name is specified. DISTINCT may not be used with COUNT(*). Similarly, DISTINCT must be used with a column name and not with a calculation or expression.

Using DISTINCT with MIN() and MAX() Although DISTINCT can technically be used with MIN() and MAX(), there is actually no value in doing so. The minimum and maximum values in a column will be the same whether or not only distinct values are included.

Additional Aggregate Arguments In addition to the DISTINCT and ALL arguments shown here, some DBMSs support additional arguments such as TOP and TOP PERCENT that let you perform calculations on subsets of query results. Refer to your DBMS documentation to determine exactly what arguments are available to you.

Combining Aggregate Functions All the examples of aggregate function used thus far have involved a single function. But actually, SELECT statements may contain as few or as many aggregate functions as needed. Look at this example:

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INPUT

OUTPUT

SELECT COUNT(*) AS num_items, MIN(prod_price) AS price_min, MAX(prod_price) AS price_max, AVG(prod_price) AS price_avg FROM Products; num_items --------9

price_min --------3.4900

price_max --------11.9900

price_avg --------6.823333

Here a single SELECT statement performs four aggregate cal-

ANALYSIS culations in one step and returns four values (the number of

items in the Products table, and the highest, lowest, and average product prices).

Naming Aliases When specifying alias names to contain the results of an aggregate function, try to not use the name of an actual column in the table. Although there is nothing actually illegal about doing so, many SQL implementations do not support this and will generate obscure error messages if you do so.

Summary Aggregate functions are used to summarize data. SQL supports five aggregate functions, all of which can be used in multiple ways to return just the results you need. These functions are designed to be highly efficient, and they usually return results far more quickly than you could calculate them yourself within your own client application.

LESSON 10

Grouping Data In this lesson, you’ll learn how to group data so that you can summarize subsets of table contents. This involves two new SELECT statement clauses: the GROUP BY clause and the HAVING clause.

Understanding Data Grouping In the last lesson, you learned that the SQL aggregate functions can be used to summarize data. This enables you to count rows, calculate sums and averages, and obtain high and low values without having to retrieve all the data. All the calculations thus far were performed on all the data in a table or on data that matched a specific WHERE clause. As a reminder, the following example returns the number of products offered by vendor DLL01:

INPUT OUTPUT

SELECT COUNT(*) AS num_prods FROM Products WHERE vend_id = ‘DLL01’; num_prods ----------4

But what if you wanted to return the number of products offered by each vendor? Or products offered by vendors who offer a single product, or only those who offer more than ten products? This is where groups come into play. Grouping lets you divide data into logical sets so that you can perform aggregate calculations on each group.

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Creating Groups Groups are created using the GROUP BY clause in your SELECT statement. The best way to understand this is to look at an example:

INPUT OUTPUT

SELECT vend_id, COUNT(*) AS num_prods FROM Products GROUP BY vend_id; vend_id --------BRS01 DLL01 FNG01

num_prods --------3 4 2

The above SELECT statement specifies two columns, vend_id, which contains the ID of a product’s vendor, and num_prods, which is a calculated field (created using the COUNT(*) function). The GROUP BY clause instructs the DBMS to sort the data and group it by vend_id. This causes num_prods to be calculated once per vend_id rather than once for the entire table. As you can see in the output, vendor BRS01 has 3 products listed, vendor DLL01 has 4 products listed, and vendor FNG01 has 2 products listed.

ANALYSIS

Because you used GROUP BY, you did not have to specify each group to be evaluated and calculated. That was done automatically. The GROUP BY clause instructs the DBMS to group the data and then perform the aggregate on each group rather than on the entire result set. Before you use GROUP you need to know: •

BY,

here are some important rules about its use that

clauses can contain as many columns as you want. This enables you to nest groups, providing you with more granular control over how data is grouped.

GROUP BY

• If you have nested groups in your GROUP BY clause, data is summarized at the last specified group. In other words, all the columns specified are evaluated together when grouping is established (so you won’t get data back for each individual column level).

Grouping Data

• Every column listed in GROUP BY must be a retrieved column or a valid expression (but not an aggregate function). If an expression is used in the SELECT, that same expression must be specified in GROUP BY. Aliases cannot be used. • Most SQL implementations do not allow GROUP BY columns with variable length datatypes (such as text or memo fields). • Aside from the aggregate calculations statements, every column in your SELECT statement must be present in the GROUP BY clause. • If the grouping column contains a row with a NULL value, NULL will be returned as a group. If there are multiple rows with NULL values, they’ll all be grouped together. • The GROUP BY clause must come after any WHERE clause and before any ORDER BY clause.

The ALL Clause Some SQL implementations (such as Microsoft SQL Server) support an optional ALL clause within GROUP BY. This clause can be used to return all groups, even those that have no matching rows (in which case the aggregate would return NULL). Refer to your DBMS documentation to see if it supports ALL.

Specifying Columns by Relative Position Some SQL implementations allow you to specify GROUP BY columns by the position in the SELECT list. For example, GROUP BY 2,1 can mean group by the second column selected and then by the first. Although this shorthand syntax is convenient, it is not supported by all SQL implementations. It’s use is also risky in that it is highly susceptible to the introduction of errors when editing SQL statements.

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Filtering Groups In addition to being able to group data using GROUP BY, SQL also allows you to filter which groups to include and which to exclude. For example, you might want a list of all customers who have made at least two orders. To obtain this data you must filter based on the complete group, not on individual rows. You’ve already seen the WHERE clause in action (that was introduced back in Lesson 4, “Filtering Data.” But WHERE does not work here because WHERE filters specific rows, not groups. As a matter of fact, WHERE has no idea what a group is. So what do you use instead of WHERE? SQL provides yet another clause for this purpose: the HAVING clause. HAVING is very similar to WHERE. In fact, all types of WHERE clauses you learned about thus far can also be used with HAVING. The only difference is that WHERE filters rows and HAVING filters groups.

HAVING Supports All of WHERE’s Operators

In Lesson 4 and Lesson 5, “Advanced Data Filtering,” you learned about WHERE clause conditions (including wildcard conditions and clauses with multiple operators). All the techniques and options that you learned about WHERE can be applied to HAVING. The syntax is identical; just the keyword is different.

So how do you filter groups? Look at the following example:

INPUT

OUTPUT

SELECT cust_id, COUNT(*) AS orders FROM Orders GROUP BY cust_id HAVING COUNT(*) >= 2; cust_id ---------1000000001

orders ----------2

Grouping Data

The first three lines of this SELECT statement are similar to the statements seen above. The final line adds a HAVING clause that filters on those groups with a COUNT(*) >= 2—two or more orders.

ANALYSIS

As you can see, a WHERE clause does not work here because the filtering is based on the group aggregate value, not on the values of specific rows.

The difference between HAVING and WHERE Here’s another way to look it: WHERE filters before data is grouped, and HAVING filters after data is grouped. This is an important distinction; rows that are eliminated by a WHERE clause will not be included in the group. This could change the calculated values which in turn could affect which groups are filtered based on the use of those values in the HAVING clause.

So is there ever a need to use both WHERE and HAVING clauses in one statement? Actually, yes, there is. Suppose you want to further filter the above statement so that it returns any customers who placed two or more orders in the past 12 months. To do that, you can add a WHERE clause that filters out just the orders placed in the past 12 months. You then add a HAVING clause to filter just the groups with two or more rows in them. To better demonstrate this, look at the following example that lists all vendors who have two or more products priced at 4 or more:

INPUT

OUTPUT

SELECT vend_id, COUNT(*) AS num_prods FROM Products WHERE prod_price >= 4 GROUP BY vend_id HAVING COUNT(*) >= 2; vend_id ---------BRS01 FNG01

num_prods ----------3 2

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This statement warrants an explanation. The first line is a basic SELECT using an aggregate function—much like the examples thus far. The WHERE clause filters all rows with a prod_price of at least 4. Data is then grouped by vend_id, and then a HAVING clause filters just those groups with a count of 2 or more. Without the WHERE clause an extra row would have been retrieved (vendor DLL01 who sells four products all priced under 4) as seen here:

ANALYSIS

INPUT

OUTPUT

SELECT vend_id, COUNT(*) AS num_prods FROM Products GROUP BY vend_id HAVING COUNT(*) >= 2; vend_id ---------BRS01 DLL01 FNG01

num_prods ----------3 4 2

Using HAVING and WHERE HAVING is so similar to WHERE that most DBMSs treat them as the same thing if no GROUP BY is specified. Nevertheless, you should make that distinction yourself. Use HAVING only in conjunction with GROUP BY clauses. Use WHERE for standard row-level filtering.

Grouping and Sorting It is important to understand that GROUP BY and ORDER BY are very different, even though they often accomplish the same thing. Table 10.1 summarizes the differences between them.

Grouping Data

TABLE 10.1

ORDER BY Versus GROUP BY

ORDER BY

GROUP BY

Sorts generated output.

Groups rows. The output might not be in group order, however.

Any columns (even columns not selected) may be used.

Only selected columns or expressions columns may be used, and every selected column expression must be used.

Never required.

Required if using columns (or expressions) with aggregate functions.

The first difference listed in Table 10.1 is extremely important. More often than not, you will find that data grouped using GROUP BY will indeed be output in group order. But that is not always the case, and it is not actually required by the SQL specifications. Furthermore, even if your particular DBMS does, in fact, always sort the data by the specified GROUP BY clause, you might actually want it sorted differently. Just because you group data one way (to obtain group specific aggregate values) does not mean that you want the output sorted that same way. You should always provide an explicit ORDER BY clause as well, even if it is identical to the GROUP BY clause.

Don’t Forget ORDER BY As a rule, anytime you use a GROUP BY clause, you should also specify an ORDER BY clause. That is the only way to ensure that data will be sorted properly. Never rely on GROUP BY to sort your data.

To demonstrate the use of both GROUP BY and ORDER BY, let’s look at an example. The following SELECT statement is similar to the ones seen previously. It retrieves the order number and number of items ordered for all orders containing three or more items:

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INPUT

OUTPUT

SELECT order_num, COUNT(*) AS items FROM OrderItems GROUP BY order_num HAVING COUNT(*) >= 3; order_num ---------20006 20007 20008 20009

items ----3 5 5 3

To sort the output by number of items ordered, all you need to do is add an ORDER BY clause, as follows:

INPUT

SELECT order_num, COUNT(*) AS items FROM OrderItems GROUP BY order_num HAVING COUNT(*) >= 3 ORDER BY items, order_num;

Access Incompatibility Microsoft Access does not allow sorting by alias, and so this example will fail. The solution is to replace items (in the ORDER BY clause) with the actual calculation or with the field position. As such, ORDER BY COUNT(*), order_num or ORDER BY 2, order_num will both work.

OUTPUT

order_num ---------20006 20009 20007 20008

items ----3 3 5 5

In this example, the GROUP BY clause is used to group the data by order number (the order_num column) so that the COUNT(*) function can return the number of items in each order. The HAVING clause filters the data so that only orders with three or more items are returned. Finally, the output is sorted using the ORDER BY clause.

ANALYSIS

Grouping Data

SELECT Clause Ordering This is probably a good time to review the order in which SELECT statement clauses are to be specified. Table 10.2 lists all the clauses we have learned thus far, in the order they must be used.

TABLE 10.2

SELECT Clauses and Their Sequence

Clause

Description

Required

SELECT

Columns or expressions to be returned

Yes

FROM

Table to retrieve data from

Only if selecting data from a table

WHERE

Row-level filtering

No

GROUP BY

Group specification

Only if calculating aggregates by group

HAVING

Group-level filtering

No

ORDER BY

Output sort order

No

Summary In Lesson 9, “Summarizing Data,” you learned how to use the SQL aggregate functions to perform summary calculations on your data. In this lesson, you learned how to use the GROUP BY clause to perform these calculations on groups of data, returning results for each group. You saw how to use the HAVING clause to filter specific groups. You also learned the difference between ORDER BY and GROUP BY and between WHERE and HAVING.

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LESSON 11

Working with Subqueries In this lesson, you’ll learn what subqueries are and how to use them.

Understanding Subqueries SELECT statements are SQL queries. All the SELECT statements we have seen thus far are simple queries: single statements retrieving data from individual database tables.

Query Any SQL statement. However, the term is usually used to refer to SELECT statements. SQL also enables you to create subqueries: queries that are embedded into other queries. Why would you want to do this? The best way to understand this concept is to look at a couple of examples.

MySQL Support If you are using MySQL, be aware that support for subqueries was introduced in version 4.1. Earlier versions of MySQL do not support subqueries.

Filtering by Subquery The database tables used in all the lessons in this book are relational tables. (See Appendix A, “Sample Data Scripts,” for a description of each of the tables and their relationships.) Orders are stored in two tables. The Orders table stores a single row for each order containing order number, customer ID, and order date. The individual order items are stored in the

related OrderItems table. The Orders table does not store customer information. It only stores a customer ID. The actual customer information is stored in the Customers table. Now suppose you wanted a list of all the customers who ordered item RGAN01. What would you have to do to retrieve this information? Here are the steps: 1. Retrieve the order numbers of all orders containing item RGAN01. 2. Retrieve the customer ID of all the customers who have orders listed in the order numbers returned in the previous step. 3. Retrieve the customer information for all the customer IDs returned in the previous step. Each of these steps can be executed as a separate query. By doing so, you use the results returned by one SELECT statement to populate the WHERE clause of the next SELECT statement. You can also use subqueries to combine all three queries into one single statement. The first SELECT statement should be self-explanatory by now. It retrieves the order_num column for all order items with a prod_id of RGAN01. The output lists the two orders containing this item:

INPUT OUTPUT

SELECT order_num FROM OrderItems WHERE prod_id = ‘RGAN01’; order_num ----------20007 20008

The next step is to retrieve the customer IDs associated with orders 20007 and 20008. Using the IN clause described in Lesson 5, “Advanced Data Filtering,” you can create a SELECT statement as follows:

INPUT

SELECT cust_id FROM Orders WHERE order_num IN (20007,20008);

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OUTPUT

cust_id ---------1000000004 1000000005

Now, combine the two queries by turning the first (the one that returned the order numbers) into a subquery. Look at the following SELECT statement:

INPUT

OUTPUT

SELECT cust_id FROM Orders WHERE order_num IN (SELECT order_num FROM OrderItems WHERE prod_id = ‘RGAN01’); cust_id ---------1000000004 1000000005

Subqueries are always processed starting with the innermost SELECT statement and working outward. When the preceding SELECT statement is processed, the DBMS actually performs two operations.

ANALYSIS

First it runs the subquery: SELECT order_num FROM orderitems WHERE prod_id=’RGAN01’

That query returns the two order numbers 20007 and 20008. Those two values are then passed to the WHERE clause of the outer query in the comma-delimited format required by the IN operator. The outer query now becomes SELECT cust_id FROM orders WHERE order_num IN (20007,20008)

As you can see, the output is correct and exactly the same as the output returned by the hard-coded WHERE clause above.

Working with Subqueries

Formatting Your SQL SELECT statements containing subqueries can be difficult to read and debug, especially as they grow in complexity. Breaking up the queries over multiple lines and indenting the lines appropriately as shown here can greatly simplify working with subqueries.

You now have the IDs of all the customers who ordered item RGAN01. The next step is to retrieve the customer information for each of those customer IDs. The SQL statement to retrieve the two columns is

INPUT

SELECT cust_name, cust_contact FROM Customers WHERE cust_id IN (‘1000000004’,’1000000005’);

Instead of hard-coding those customer IDs, you can turn this WHERE clause into a subquery:

INPUT

OUTPUT

SELECT cust_name, cust_contact FROM Customers WHERE cust_id IN (SELECT cust_id FROM Orders WHERE order_num IN (SELECT order_num FROM OrderItems WHERE prod_id = ‘RGAN01’)); cust_name ----------------------------Fun4All The Toy Store

cust_contact -----------------Denise L. Stephens Kim Howard

To execute the above SELECT statement, the DBMS had to actually perform three SELECT statements. The innermost subquery returned a list of order numbers that were then used as the WHERE clause for the subquery above it. That subquery returned a list of customer IDs that were used as the WHERE clause for the top-level query. The toplevel query actually returned the desired data.

ANALYSIS

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As you can see, using subqueries in a WHERE clause enables you to write extremely powerful and flexible SQL statements. There is no limit imposed on the number of subqueries that can be nested, although in practice you will find that performance will tell you when you are nesting too deeply.

Single Column Only Subquery SELECT statements can only retrieve a single column. Attempting to retrieve multiple columns will return an error.

Subqueries and Performance The code shown here works, and it achieves the desired result. However, using subqueries is not always the most efficient way to perform this type of data retrieval. More on this in Lesson 12, “Joining Tables,” where you will revisit this same example.

Using Subqueries As Calculated Fields Another way to use subqueries is in creating calculated fields. Suppose you want to display the total number of orders placed by every customer in your Customers table. Orders are stored in the Orders table along with the appropriate customer ID. To perform this operation, follow these steps: 1. Retrieve the list of customers from the Customers table. 2. For each customer retrieved, count the number of associated orders in the Orders table. As you learned in the previous two lessons, you can use SELECT COUNT(*) to count rows in a table, and by providing a WHERE clause to filter a specific customer ID, you can count just that customer’s orders. For example,

Working with Subqueries

the following code counts the number of orders placed by customer 1000000001:

INPUT

SELECT COUNT(*) AS orders FROM Orders WHERE cust_id = ‘1000000001’;

To perform that COUNT(*) calculation for each customer, use COUNT* as a subquery. Look at the following code:

INPUT

OUTPUT

SELECT cust_name, cust_state, (SELECT COUNT(*) FROM Orders WHERE Orders.cust_id = Customers.cust_id) AS orders FROM Customers ORDER BY cust_name; cust_name ------------------------Fun4All Fun4All Kids Place The Toy Store Village Toys

cust_state ---------IN AZ OH IL MI

orders -----1 1 0 1 2

This SELECT statement returns three columns for every customer in the Customers table: cust_name, cust_state, and orders. Orders is a calculated field that is set by a subquery that is provided in parentheses. That subquery is executed once for every customer retrieved. In the example above, the subquery is executed five times because five customers were retrieved.

ANALYSIS

The WHERE clause in the subquery is a little different from the WHERE clauses used previously because it uses fully qualified column names. The following clause tells SQL to compare the cust_id in the Orders table to the one currently being retrieved from the Customers table: WHERE Orders.cust_id = Customers.cust_id

This syntax—the table name and the column name separated by a period—must be used whenever there is possible ambiguity about column names. In this example, there are two cust_id columns, one in Customers and one in Orders. Without fully qualifying the column names, the

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DBMS assumes you are comparing the cust_id in the Orders table to itself. Because SELECT COUNT(*) FROM Orders WHERE cust_id = cust_id

will always return the total number of orders in the Orders table, the results will not be what you expected:

INPUT

OUTPUT

SELECT cust_name, cust_state, (SELECT COUNT(*) FROM Orders WHERE cust_id = cust_id) AS orders FROM Customers ORDER BY cust_name; cust_name ------------------------Fun4All Fun4All Kids Place The Toy Store Village Toys

cust_state ---------IN AZ OH IL MI

orders -----5 5 5 5 5

Although subqueries are extremely useful in constructing this type of SELECT statement, care must be taken to properly qualify ambiguous column names.

Always More Than One Solution As explained earlier in this lesson, although the sample code shown here works, it is often not the most efficient way to perform this type of data retrieval. You will revisit this example in a later lesson.

Summary In this lesson, you learned what subqueries are and how to use them. The most common uses for subqueries are in WHERE clause IN operators and for populating calculated columns. You saw examples of both of these types of operations.

LESSON 12

Joining Tables In this lesson, you’ll learn what joins are, why they are used, and how to create SELECT statements using them.

Understanding Joins One of SQL’s most powerful features is the capability to join tables on-thefly within data retrieval queries. Joins are one of the most important operations that you can perform using SQL SELECT, and a good understanding of joins and join syntax is an extremely important part of learning SQL. Before you can effectively use joins, you must understand relational tables and the basics of relational database design. What follows is by no means complete coverage of the subject, but it should be enough to get you up and running.

Understanding Relational Tables The best way to understand relational tables is to look at a real-world example. Suppose you had a database table containing a product catalog, with each catalog item in its own row. The kind of information you would store with each item would include a product description and price, along with vendor information about the company that creates the product. Now suppose that you had multiple catalog items created by the same vendor. Where would you store the vendor information (things like vendor name, address, and contact information)? You wouldn’t want to store that data along with the products for several reasons: • Because the vendor information is the same for each product that vendor produces, repeating the information for each product is a waste of time and storage space.

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• If vendor information changes (for example, if the vendor moves or his area code changes), you would need to update every occurrence of the vendor information. • When data is repeated (that is, the vendor information is used with each product), there is a high likelihood that the data will not be entered exactly the same way each time. Inconsistent data is extremely difficult to use in reporting. The key here is that having multiple occurrences of the same data is never a good thing, and that principle is the basis for relational database design. Relational tables are designed so that information is split into multiple tables, one for each data type. The tables are related to each other through common values (and thus the relational in relational design). In our example, you can create two tables, one for vendor information and one for product information. The Vendors table contains all the vendor information, one table row per vendor, along with a unique identifier for each vendor. This value, called a primary key, can be a vendor ID, or any other unique value. The Products table stores only product information, and no vendor specific information other than the vendor ID (the Vendors table’s primary key). This key relates the Vendors table to the Products table, and using this vendor ID enables you to use the Vendors table to find the details about the appropriate vendor. What does this do for you? Well, consider the following: • Vendor information is never repeated, and so time and space are not wasted. • If vendor information changes, you can update a single record, the one in the Vendors table. Data in related tables does not change. • As no data is repeated, the data used is obviously consistent, making data reporting and manipulation much simpler. The bottom line is that relational data can be stored efficiently and manipulated easily. Because of this, relational databases scale far better than nonrelational databases.

Joining Tables

Scale Able to handle an increasing load without failing. A well-designed database or application is said to scale well.

Why Use Joins? As just explained, breaking data into multiple tables enables more efficient storage, easier manipulation, and greater scalability. But these benefits come with a price. If data is stored in multiple tables, how can you retrieve that data with a single SELECT statement? The answer is to use a join. Simply put, a join is a mechanism used to associate tables within a SELECT statement (and thus the name join). Using a special syntax, multiple tables can be joined so that a single set of output is returned, and the join associates the correct rows in each table onthe-fly.

Using Interactive DBMS Tools It is important to understand that a join is not a physical entity—in other words, it does not exist in the actual database tables. A join is created by the DBMS as needed, and it persists for the duration of the query execution. Many DBMSs provide graphical interfaces that can be used to define table relationships interactively. These tools can be invaluable in helping to maintain referential integrity. When using relational tables, it is important that only valid data is inserted into relational columns. Going back to the example, if an invalid vendor ID is stored in the Products table, those products would be inaccessible because they would not be related to any vendor. To prevent this from occurring, the database can be instructed to only allow valid values (ones present in the Vendors table) in the vendor ID column in the Products table. Referential integrity means that the DBMS enforces data integrity rules. And these rules are often managed through DBMS provided interfaces.

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Creating a Join Creating a join is very simple. You must specify all the tables to be included and how they are related to each other. Look at the following example:

INPUT OUTPUT

SELECT vend_name, prod_name, prod_price FROM Vendors, Products WHERE Vendors.vend_id = Products.vend_id; vend_name --------Doll House Inc. Doll House Inc. Doll House Inc. Bears R Us Bears R Us Bears R Us Doll House Inc. Fun and Games Fun and Games

prod_name ---------Fish bean bag toy Bird bean bag toy Rabbit bean bag toy 8 inch teddy bear 12 inch teddy bear 18 inch teddy bear Raggedy Ann King doll Queen doll

prod_price ---------3.4900 3.4900 3.4900 5.9900 8.9900 11.9900 4.9900 9.4900 9.4900

Let’s take a look at the preceding code. The SELECT statement starts in the same way as all the statements you’ve looked at thus far, by specifying the columns to be retrieved. The big difference here is that two of the specified columns (prod_name and prod_price) are in one table, whereas the other (vend_name) is in another table.

ANALYSIS

Now look at the FROM clause. Unlike all the prior SELECT statements, this one has two tables listed in the FROM clause, Vendors and Products. These are the names of the two tables that are being joined in this SELECT statement. The tables are correctly joined with a WHERE clause that instructs the DBMS to match vend_id in the Vendors table with vend_id in the Products table. You’ll notice that the columns are specified as Vendors.vend_id and Products.vend_id. This fully qualified column name is required here because if you just specified vend_id, the DBMS cannot tell which vend_id columns you are referring to. (There are two of them, one in each table.) As you can see in the preceding output, a single SELECT statement returns data from two different tables.

Joining Tables

Fully Qualifying Column Names You must use the fully qualified column name (table and column separated by a period) whenever there is a possible ambiguity about which column you are referring to. Most DBMSs will return an error message if you refer to an ambiguous column name without fully qualifying it with a table name.

The Importance of the WHERE Clause It might seem strange to use a WHERE clause to set the join relationship, but actually, there is a very good reason for this. Remember, when tables are joined in a SELECT statement, that relationship is constructed on-thefly. There is nothing in the database table definitions that can instruct the DBMS how to join the tables. You have to do that yourself. When you join two tables, what you are actually doing is pairing every row in the first table with every row in the second table. The WHERE clause acts as a filter to only include rows that match the specified filter condition—the join condition, in this case. Without the WHERE clause, every row in the first table will be paired with every row in the second table, regardless of if they logically go together or not. Cartesian Product The results returned by a table relationship without a join condition. The number of rows retrieved will be the number of rows in the first table multiplied by the number of rows in the second table. To understand this, look at the following SELECT statement and output:

INPUT OUTPUT

SELECT vend_name, prod_name, prod_price FROM Vendors, Products; vend_name ----------Bears R Us Bears R Us Bears R Us Bears R Us Bears R Us Bears R Us

prod_name --------8 inch teddy bear 12 inch teddy bear 18 inch teddy bear Fish bean bag toy Bird bean bag toy Rabbit bean bag toy

prod_price ---------5.99 8.99 11.99 3.49 3.49 3.49

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Bears R Us Bears R Us Bears R Us Bear Emporium Bear Emporium Bear Emporium Bear Emporium Bear Emporium Bear Emporium Bear Emporium Bear Emporium Bear Emporium Doll House Inc. Doll House Inc. Doll House Inc. Doll House Inc. Doll House Inc. Doll House Inc. Doll House Inc. Doll House Inc. Doll House Inc. Furball Inc. Furball Inc. Furball Inc. Furball Inc. Furball Inc. Furball Inc. Furball Inc. Furball Inc. Furball Inc. Fun and Games Fun and Games Fun and Games Fun and Games Fun and Games Fun and Games Fun and Games Fun and Games Fun and Games Jouets et ours Jouets et ours Jouets et ours Jouets et ours Jouets et ours Jouets et ours

Raggedy Ann King doll Queen doll 8 inch teddy bear 12 inch teddy bear 18 inch teddy bear Fish bean bag toy Bird bean bag toy Rabbit bean bag toy Raggedy Ann King doll Queen doll 8 inch teddy bear 12 inch teddy bear 18 inch teddy bear Fish bean bag toy Bird bean bag toy Rabbit bean bag toy Raggedy Ann King doll Queen doll 8 inch teddy bear 12 inch teddy bear 18 inch teddy bear Fish bean bag toy Bird bean bag toy Rabbit bean bag toy Raggedy Ann King doll Queen doll 8 inch teddy bear 12 inch teddy bear 18 inch teddy bear Fish bean bag toy Bird bean bag toy Rabbit bean bag toy Raggedy Ann King doll Queen doll 8 inch teddy bear 12 inch teddy bear 18 inch teddy bear Fish bean bag toy Bird bean bag toy Rabbit bean bag toy

4.99 9.49 9.49 5.99 8.99 11.99 3.49 3.49 3.49 4.99 9.49 9.49 5.99 8.99 11.99 3.49 3.49 3.49 4.99 9.49 9.49 5.99 8.99 11.99 3.49 3.49 3.49 4.99 9.49 9.49 5.99 8.99 11.99 3.49 3.49 3.49 4.99 9.49 9.49 5.99 8.99 11.99 3.49 3.49 3.49

Joining Tables

Jouets et ours Jouets et ours Jouets et ours

Raggedy Ann King doll Queen doll

4.99 9.49 9.49

As you can see in the preceding output, the Cartesian product is seldom what you want. The data returned here has matched every product with every vendor, including products with the incorrect vendor (and even vendors with no products at all).

ANALYSIS

Don’t Forget the WHERE Clause Make sure all your joins have WHERE clauses, or the DBMS will return far more data than you want. Similarly, make sure your WHERE clauses are correct. An incorrect filter condition will cause the DBMS to return incorrect data.

Cross Joins Sometimes you’ll hear the type of join that returns a Cartesian Product referred to as a cross join.

Inner Joins The join you have been using so far is called an equijoin—a join based on the testing of equality between two tables. This kind of join is also called an inner join. In fact, you may use a slightly different syntax for these joins, specifying the type of join explicitly. The following SELECT statement returns the exact same data as the preceding example:

INPUT

SELECT vend_name, prod_name, prod_price FROM Vendors INNER JOIN Products ON Vendors.vend_id = Products.vend_id;

The SELECT in the statement is the same as the preceding SELECT statement, but the FROM clause is different. Here the relationship between the two tables is part of the FROM clause specified as INNER JOIN. When using this syntax the join condition is specified using

ANALYSIS

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the special ON clause instead of a WHERE clause. The actual condition passed to ON is the same as would be passed to WHERE. Refer to your DBMS documentation to see which syntax is preferred.

The “Right” Syntax Per the ANSI SQL specification, use of the INNER JOIN syntax is preferable.

Joining Multiple Tables SQL imposes no limit to the number of tables that may be joined in a SELECT statement. The basic rules for creating the join remain the same. First list all the tables, and then define the relationship between each. Here is an example:

INPUT

SELECT prod_name, vend_name, prod_price, quantity FROM OrderItems, Products, Vendors WHERE Products.vend_id = Vendors.vend_id AND OrderItems.prod_id = Products.prod_id AND order_num = 20007;

OUTPUT prod_name --------18 inch teddy bear Fish bean bag toy Bird bean bag toy Rabbit bean bag toy Raggedy Ann

vend_name --------Bears R Us Doll House Doll House Doll House Doll House

Inc. Inc. Inc. Inc.

prod_price ---------11.9900 3.4900 3.4900 3.4900 4.9900

quantity -------50 100 100 100 50

This example displays the items in order number 20007. Order items are stored in the OrderItems table. Each product is stored by its product ID, which refers to a product in the Products table. The products are linked to the appropriate vendor in the Vendors table by the vendor ID, which is stored with each product record. The FROM clause here lists the three tables, and the WHERE clause defines both of those join conditions. An additional WHERE condition is then used to filter just the items for order 20007.

ANALYSIS

Joining Tables

Performance Considerations DBMSs process joins at run-time relating each table as specified. This process can become very resource intensive so be careful not to join tables unnecessarily. The more tables you join the more performance will degrade.

Maximum Number of Tables in a Join While it is true that SQL itself has no maximum number of tables per join restriction, many DBMSs do indeed have restrictions. Refer to your DBMS documentation to determine what restrictions there are, if any.

Now would be a good time to revisit the following example from Lesson 11, “Working with Subqueries.” As you will recall, this SELECT statement returns a list of customers who ordered product RGAN01:

INPUT

SELECT cust_name, cust_contact FROM Customers WHERE cust_id IN (SELECT cust_id FROM Orders WHERE order_num IN (SELECT order_num FROM OrderItems WHERE prod_id = ‘RGAN01’));

As I mentioned in Lesson 11, subqueries are not always the most efficient way to perform complex SELECT operations, and so as promised, here is the same query using joins:

INPUT

OUTPUT

SELECT cust_name, cust_contact FROM Customers, Orders, OrderItems WHERE Customers.cust_id = Orders.cust_id AND OrderItems.order_num = Orders.order_num AND prod_id = ‘RGAN01’; cust_name ----------------------------Fun4All The Toy Store

cust_contact ------------------Denise L. Stephens Kim Howard

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ANALYSIS As explained in Lesson 11, returning the data needed in this

query requires the use of three tables. But instead of using them within nested subqueries, here two joins are used to connect the tables. There are three WHERE clause conditions here. The first two connect the tables in the join, and the last one filters the data for product RGAN01.

It Pays to Experiment As you can see, there is often more than one way to perform any given SQL operation. And there is rarely a definitive right or wrong way. Performance can be affected by the type of operation, the DBMS being used, the amount of data in the tables, whether or not indexes and keys are present, and a whole slew of other criteria. Therefore, it is often worth experimenting with different selection mechanisms to find the one that works best for you.

Summary Joins are one of the most important and powerful features in SQL, and using them effectively requires a basic understanding of relational database design. In this lesson, you learned some of the basics of relational database design as an introduction to learning about joins. You also learned how to create an equijoin (also known as an inner join), which is the most commonly used form of join. In the next, lesson, you’ll learn how to create other types of joins.

LESSON 13

Creating Advanced Joins In this lesson, you’ll learn all about additional join types—what they are, and how to use them. You’ll also learn how to use table aliases and how to use aggregate functions with joined tables.

Using Table Aliases Back in Lesson 7, “Creating Calculated Fields,” you learned how to use aliases to refer to retrieved table columns. The syntax to alias a column looks like this:

INPUT

SELECT RTRIM(vend_name) + ‘ (‘ + RTRIM(vend_country) ➥+ ‘)’ AS vend_title FROM Vendors ORDER BY vend_name;

In addition to using aliases for column names and calculated fields, SQL also enables you to alias table names. There are two primary reasons to do this: • To shorten the SQL syntax • To enable multiple uses of the same table within a single SELECT statement

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Take a look at the following SELECT statement. It is basically the same statement as an example used in the previous lesson, but it has been modified to use aliases:

INPUT

SELECT cust_name, cust_contact FROM Customers AS C, Orders AS O, OrderItems AS OI WHERE C.cust_id = O.cust_id AND OI.order_num = O.order_num AND prod_id = ‘RGAN01’;

You’ll notice that the three tables in the FROM clauses all have aliases. Customers AS C establishes C as an alias for Customers, and so on. This enables you to use the abbreviated C instead of the full text Customers. In this example, the table aliases were used only in the WHERE clause, but aliases are not limited to just WHERE. You can use aliases in the SELECT list, the ORDER BY clause, and in any other part of the statement as well.

ANALYSIS

No AS in Oracle Oracle does not support the AS keyword. To use aliases in Oracle, simply specify the alias without AS (so Customers C instead of Customers AS C).

It is also worth noting that table aliases are only used during query execution. Unlike column aliases, table aliases are never returned to the client.

Using Different Join Types So far, you have used only simple joins known as inner joins or equijoins. You’ll now take a look at three additional join types: the self join, the natural join, and the outer join.

Self Joins As I mentioned earlier, one of the primary reasons to use table aliases is to be able to refer to the same table more than once in a single SELECT statement. An example will demonstrate this.

Creating Advanced Joins

Suppose you wanted to send a mailing to all the customer contacts who work for the same company for which Jim Jones works. This query requires that you first find out which company Jim Jones works for, and next which customers work for that company. The following is one way to approach this problem:

INPUT

OUTPUT

SELECT cust_id, cust_name, cust_contact FROM Customers WHERE cust_name = (SELECT cust_name FROM Customers WHERE cust_contact = ‘Jim Jones’); cust_id -------1000000003 1000000004

cust_name -------------Fun4All Fun4All

cust_contact -------------Jim Jones Denise L. Stephens

This first solution uses subqueries. The inner SELECT statement does a simple retrieval to return the cust_name of the company that Jim Jones works for. That name is the one used in the WHERE clause of the outer query so that all employees who work for that company are retrieved. (You learned all about subqueries in Lesson 11, “Working with Subqeries.” Refer to that lesson for more information.)

ANALYSIS

Now look at the same query using a join:

INPUT

OUTPUT

SELECT c1.cust_id, c1.cust_name, c1.cust_contact FROM Customers AS c1, Customers AS c2 WHERE c1.cust_name = c2.cust_name AND c2.cust_contact = ‘Jim Jones’; cust_id ------1000000003 1000000004

No AS in Oracle the AS.

cust_name ----------Fun4All Fun4All

cust_contact -------------Jim Jones Denise L. Stephens

Oracle users, remember to drop

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The two tables needed in this query are actually the same table, and so the Customers table appears in the FROM clause twice. Although this is perfectly legal, any references to table Customers would be ambiguous because the DBMS does not know which Customers table you are referring to.

ANALYSIS

To resolve this problem table aliases are used. The first occurrence of Customers has an alias of C1, and the second has an alias of C2. Now those aliases can be used as table names. The SELECT statement, for example, uses the C1 prefix to explicitly state the full name of the desired columns. If it did not, the DBMS would return an error because there are two columns named cust_id, cust_name, and cust_contact. It cannot know which one you want (even though, in truth, they are one and the same). The WHERE clause first joins the tables, and then it filters the data by cust_contact in the second table to return only the desired data.

Self Joins Instead of Subqueries Self joins are often used to replace statements using subqueries that retrieve data from the same table as the outer statement. Although the end result is the same, many DBMSs process joins far more quickly than they do subqueries. It is usually worth experimenting with both to determine which performs better.

Natural Joins Whenever tables are joined, at least one column will appear in more than one table (the columns being joined). Standard joins (the inner joins that you learned about in the last lesson) return all data, even multiple occurrences of the same column. A natural join simply eliminates those multiple occurrences so that only one of each column is returned. How does it do this? The answer is it doesn’t—you do it. A natural join is a join in which you select only columns that are unique. This is typically done using a wildcard (SELECT *) for one table and explicit subsets of the columns for all other tables. The following is an example:

Creating Advanced Joins

INPUT

SELECT C.*, O.order_num, O.order_date, OI.prod_id, ➥OI.quantity, OI.item_price FROM Customers AS C, Orders AS O, OrderItems AS OI WHERE C.cust_id = O.cust_id AND OI.order_num = O.order_num AND prod_id = ‘RGAN01’;

No AS in Oracle the AS.

Oracle users, remember to drop

In this example, a wildcard is used for the first table only. All other columns are explicitly listed so that no duplicate columns are retrieved.

ANALYSIS

The truth is, every inner join you have created thus far is actually a natural join, and you will probably never even need an inner join that is not a natural join.

Outer Joins Most joins relate rows in one table with rows in another. But occasionally, you will want to include rows that have no related rows. For example, you might use joins to accomplish the following tasks: • Count how many orders each customer, including customers who have yet to place an order, placed • List all products with order quantities, including products not ordered by anyone • Calculate average sale sizes, taking into account customers who have not yet placed an order In each of these examples, the join includes table rows that have no associated rows in the related table. This type of join is called an outer join.

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Syntax Differences It is important to note that the syntax used to create an outer join can vary slightly among different SQL implementations. The various forms of syntax described in the following section cover most implementations, but refer to your DBMS documentation to verify its syntax before proceeding.

The following SELECT statement is a simple inner join. It retrieves a list of all customers and their orders:

INPUT

SELECT Customers.cust_id, Orders.order_num FROM Customers INNER JOIN Orders ON Customers.cust_id = Orders.cust_id;

Outer join syntax is similar. To retrieve a list of all customers, including those who have placed no orders, you can do the following:

INPUT OUTPUT

SELECT Customers.cust_id, Orders.order_num FROM Customers LEFT OUTER JOIN Orders ON Customers.cust_id = Orders.cust_id; cust_id ---------1000000001 1000000001 1000000002 1000000003 1000000004 1000000005

order_num --------20005 20009 NULL 20006 20007 20008

Like the inner join seen in the last lesson, this SELECT statement uses the keywords OUTER JOIN to specify the join type (instead of specifying it in the WHERE clause). But unlike inner joins, which relate rows in both tables, outer joins also include rows with no related rows. When using OUTER JOIN syntax you must use the RIGHT or LEFT keywords to specify the table from which to include all rows (RIGHT for the one on the right of OUTER JOIN, and LEFT for the one on the left). The previous example uses LEFT OUTER JOIN to select all the rows from the table on the left in the FROM clause (the Customers table). To select all

ANALYSIS

Creating Advanced Joins

the rows from the table on the right, you use a RIGHT in this next example:

INPUT

OUTER JOIN

as seen

SELECT Customers.cust_id, Orders.order_num FROM Customers RIGHT OUTER JOIN Orders ON Orders.cust_id = Customers.cust_id;

SQL Server supports an additional simplified outer join syntax. To retrieve a list of all customers, including those who have placed no orders, you can do the following:

INPUT OUTPUT

SELECT Customers.cust_id, Orders.order_num FROM Customers, Orders WHERE Customers.cust_id *= Orders.cust_id; cust_id ---------1000000001 1000000001 1000000002 1000000003 1000000004 1000000005

order_num --------20005 20009 NULL 20006 20007 20008

Here the join condition is specified in the WHERE clause. Instead of testing for equality with a =, the *= operator is used to specify that every row in the Customers table should be included. *= is the left outer join operator. It retrieves all the rows from the left table.

ANALYSIS

The opposite of this left outer join is the right outer join specified by the operator. It can be used to return all rows from the table listed to the right of the operator, as seen in this next example:

=*

INPUT

SELECT Customers.cust_id, Orders.order_num FROM Customers, Orders WHERE Orders.cust_id =* Customers.cust_id;

Yet another form of the OUTER JOIN syntax (used only by Oracle) requires the use of (+) operator after the table name as follows:

INPUT

SELECT Customers.cust_id, Orders.order_num FROM Customers, Orders WHERE Customers.cust_id (+) = Orders.cust_id

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Outer Join Types Regardless of the form of outer join used, there are always two basic forms of outer joins—the left outer join and the right outer join. The only difference between them is the order of the tables that they are relating. In other words, a left outer join can be turned into a right outer join simply by reversing the order of the tables in the FROM or WHERE clause. As such, the two types of outer join can be used interchangeably, and the decision about which one is used is based purely on convenience.

There is one other variant of the outer join, and that is the full outer join that retrieves all rows from both tables and relates those that can be related. Unlike a left outer join or right outer join, which includes unrelated rows from a single table, the full outer join includes unrelated rows from both tables. The syntax for a full outer join is as follows:

INPUT

SELECT Customers.cust_id, Orders.order_num FROM Orders FULL OUTER JOIN Customers ON Orders.cust_id = Customers.cust_id;

FULL OUTER JOIN Support The FULL OUTER JOIN syntax is not supported by Access, MySQL, SQL Server, or Sybase.

Using Joins with Aggregate Functions As you learned in Lesson 9, “Summarizing Data,” aggregate functions are used to summarize data. Although all the examples of aggregate functions thus far only summarized data from a single table, these functions can also be used with joins. To demonstrate this, let’s look at an example. You want to retrieve a list of all customers and the number of orders that each has placed. The following code uses the COUNT() function to achieve this:

Creating Advanced Joins

INPUT

OUTPUT

SELECT Customers.cust_id, COUNT(Orders.order_num) AS ➥num_ord FROM Customers INNER JOIN Orders ON Customers.cust_id = Orders.cust_id GROUP BY Customers.cust_id; cust_id ---------1000000001 1000000003 1000000004 1000000005

num_ord -------2 1 1 1

This SELECT statement uses INNER JOIN to relate the Customers and Orders tables to each other. The GROUP BY clause groups the data by customer, and so the function call COUNT(Orders.order_num) counts the number of orders for each customer and returns it as num_ord.

ANALYSIS

Aggregate functions can be used just as easily with other join types. See the following example:

INPUT

SELECT Customers.cust_id, COUNT(Orders.order_num) AS ➥num_ord FROM Customers LEFT OUTER JOIN Orders ON Customers.cust_id = Orders.cust_id GROUP BY Customers.cust_id;

No AS in Oracle drop the AS.

OUTPUT

cust_id ---------1000000001 1000000002 1000000003 1000000004 1000000005

Again, Oracle users, remember to

num_ord ------2 0 1 1 1

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This example uses a left outer join to include all customers, even those who have not placed any orders. The results show that customer 1000000002 is also included, this time with 0 orders.

ANALYSIS

Using Joins and Join Conditions Before I wrap up our two lesson discussion on joins, I think it is worthwhile to summarize some key points regarding joins and their use: • Pay careful attention to the type of join being used. More often than not, you’ll want an inner join, but there are often valid uses for outer joins, too. • Check your DBMSs documentation for the exact join syntax it supports. (Most DBMSs use one of the forms of syntax described in these two lessons.) • Make sure you use the correct join condition (regardless of the syntax being used), or you’ll return incorrect data. • Make sure you always provide a join condition, or you’ll end up with the Cartesian product. • You may include multiple tables in a join and even have different join types for each. Although this is legal and often useful, make sure you test each join separately before testing them together. This will make troubleshooting far simpler.

Summary This lesson was a continuation of the last lesson on joins. This lesson started by teaching you how and why to use aliases, and then continued with a discussion on different join types and various forms of syntax used with each. You also learned how to use aggregate functions with joins, and some important do’s and don’ts to keep in mind when working with joins.

LESSON 14

Combining Queries In this lesson, you’ll learn how to use the UNION operator to combine multiple SELECT statements into one result set.

Understanding Combined Queries Most SQL queries contain a single SELECT statement that returns data from one or more tables. SQL also enables you to perform multiple queries (multiple SELECT statements) and return the results as a single query result set. These combined queries are usually known as unions or compound queries. There are basically two scenarios in which you’d use combined queries: • To return similarly structured data from different tables in a single query • To perform multiple queries against a single table returning the data as one query

Combining Queries and Multiple WHERE Conditions For the most part, combining two queries to the same table accomplishes the same thing as a single query with multiple WHERE clause conditions. In other words, any SELECT statement with multiple WHERE clauses can also be specified as a combined query, as you’ll see in the section that follows.

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Creating Combined Queries SQL queries are combined using the UNION operator. Using UNION, multiple SELECT statements can be specified, and their results can be combined into a single result set.

Using UNION Using UNION is simple enough. All you do is specify each SELECT statement and place the keyword UNION between each. Let’s look at an example. You need a report on all your customers in Illinois, Indiana, and Michigan. You also want to include all Fun4All locations, regardless of state. Of course, you can create a WHERE clause that will do this, but this time you’ll use a UNION instead. As I just explained, creating a UNION involves writing multiple SELECT statements. First look at the individual statements:

INPUT OUTPUT

INPUT OUTPUT

SELECT cust_name, cust_contact, cust_email FROM Customers WHERE cust_state IN (‘IL’,’IN’,’MI’); cust_name ----------Village Toys Fun4All The Toy Store

cust_contact ------------John Smith Jim Jones Kim Howard

cust_email [email protected] [email protected] NULL

SELECT cust_name, cust_contact, cust_email FROM Customers WHERE cust_name = ‘Fun4All’; cust_name --------Fun4All Fun4All

cust_contact -----------Jim Jones Denise L. Stephens

cust_email [email protected] [email protected]

The first SELECT retrieves all rows in Illinois, Indiana, and

ANALYSIS Michigan by passing those state abbreviations to the IN

clause. The second SELECT uses a simple equality test to find all Fun4All locations.

Combining Queries

To combine these two statements, do the following:

INPUT

SELECT cust_name, cust_contact, cust_email FROM Customers WHERE cust_state IN (‘IL’,’IN’,’MI’) UNION SELECT cust_name, cust_contact, cust_email FROM Customers WHERE cust_name = ‘Fun4All’;

OUTPUT cust_name --------Fun4All Fun4All Village Toys The Toy Store

cust_contact -----------Denise L. Stephens Jim Jones John Smith Kim Howard

cust_email [email protected] [email protected] [email protected] NULL

The preceding statements are made up of both of the previ-

ANALYSIS ous SELECT statements separated by the UNION keyword.

instructs the DBMS to execute both SELECT statements and combine the output into a single query result set. UNION

As a point of reference, here is the same query using multiple WHERE clauses instead of a UNION:

INPUT

SELECT cust_name, cust_contact, cust_email FROM Customers WHERE cust_state IN (‘IL’,’IN’,’MI’) OR cust_name = ‘Fun4All’;

In our simple example, the UNION might actually be more complicated than using a WHERE clause. But with more complex filtering conditions, or if the data is being retrieved from multiple tables (and not just a single table), the UNION could have made the process much simpler indeed.

UNION Limits There is no standard SQL limit to the number of SELECT statements that can be combined with UNION statements. However, it is best to consult your DBMS documentation to ensure that it does not enforce any maximum statement restrictions of its own.

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Performance Issues Most good DBMSs use an internal query optimizer to combine the SELECT statements before they are even processed. In theory, this means that from a performance perspective, there should be no real difference between using multiple WHERE clause conditions or a UNION. I say in theory, because, in practice, most query optimizers don’t always do as good a job as they should. Your best bet is to test both methods to see which will work best for you.

UNION Rules As you can see, unions are very easy to use. But there are a few rules governing exactly which can be combined: • A UNION must be comprised of two or more SELECT statements, each separated by the keyword UNION (so, if combining four SELECT statements there would be three UNION keywords used). • Each query in a UNION must contain the same columns, expressions, or aggregate functions (although columns need not be listed in the same order). • Column datatypes must be compatible: They need not be the exact same type, but they must be of a type that the DBMS can implicitly convert (for example, different numeric types or different date types). Aside from these basic rules and restrictions, unions can be used for any data retrieval tasks.

Including or Eliminating Duplicate Rows Go back to the preceding section titled “Using UNION” and look at the sample SELECT statements used. You’ll notice that when executed individually, the first SELECT statement returns three rows, and the second SELECT statement returns two rows. However, when the two SELECT statements are combined with a UNION, only four rows are returned, not five.

Combining Queries

The UNION automatically removes any duplicate rows from the query result set (in other words, it behaves just as do multiple WHERE clause conditions in a single SELECT would). Because there is a Fun4All location in Indiana, that row was returned by both SELECT statements. When the UNION was used the duplicate row was eliminated. This is the default behavior of UNION, but you can change this if you so desire. If you would, in fact, want all occurrences of all matches returned, you can use UNION ALL instead of UNION. Look at the following example:

INPUT

SELECT cust_name, cust_contact, cust_email FROM Customers WHERE cust_state IN (‘IL’,’IN’,’MI’) UNION ALL SELECT cust_name, cust_contact, cust_email FROM Customers WHERE cust_name = ‘Fun4All’;

OUTPUT cust_name ----------Village Toys Fun4All The Toy Store Fun4All Fun4All

cust_contact -----------John Smith Jim Jones Kim Howard Jim Jones Denise L. Stephens

cust_email [email protected] [email protected] NULL [email protected] [email protected]

Using UNION ALL, the DBMS does not eliminate duplicates. Therefore, the preceding example returns five rows, one of them occurring twice.

ANALYSIS

UNION versus WHERE At the beginning of this lesson, I said that UNION almost always accomplishes the same thing as multiple WHERE conditions. UNION ALL is the form of UNION that accomplishes what cannot be done with WHERE clauses. If you do, in fact, want all occurrences of matches for every condition (including duplicates), you must use UNION ALL and not WHERE.

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Sorting Combined Query Results statement output is sorted using the ORDER BY clause. When combining queries with a UNION only one ORDER BY clause may be used, and it must occur after the final SELECT statement. There is very little point in sorting part of a result set one way and part another way, and so multiple ORDER BY clauses are not allowed. SELECT

The following example sorts the results returned by the previously used UNION:

INPUT SELECT cust_name, cust_contact, cust_email FROM Customers WHERE cust_state IN (‘IL’,’IN’,’MI’) UNION SELECT cust_name, cust_contact, cust_email FROM Customers WHERE cust_name = ‘Fun4All’ ORDER BY cust_name, cust_contact;

OUTPUT cust_name ----------Fun4All Fun4All The Toy Store Village Toys

cust_contact -----------Denise L. Stephens Jim Jones Kim Howard John Smith

cust_email [email protected] [email protected] NULL [email protected]

ANALYSIS This UNION takes a single ORDER BY clause after the final SELECT statement. Even though the ORDER BY appears to only be a part of that last SELECT statement, the DBMS will in fact use it to sort all the results returned by all the SELECT statements.

Combining Queries

Other UNION Types Some DBMSs support two additional types of UNION. EXCEPT (sometimes called MINUS) can be used to only retrieve the rows that exist in the first table but not in the second, and INTERSECT can be used to retrieve only the rows that exist in both tables. In practice, however, these UNION types are rarely used as the same results can be accomplished using joins.

Summary In this lesson, you learned how to combine SELECT statements with the UNION operator. Using UNION, you can return the results of multiple queries as one combined query, either including or excluding duplicates. The use of UNION can greatly simplify complex WHERE clauses and retrieving data from multiple tables.

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LESSON 15

Inserting Data In this lesson, you will learn how to insert data into tables using the SQL INSERT statement.

Understanding Data Insertion SELECT is undoubtedly the most frequently used SQL statement (which is why the last 14 lessons were dedicated to it). But there are three other frequently used SQL statements that you should learn. The first one is INSERT. (You’ll get to the other two in the next lesson.)

As its name suggests, INSERT is used to insert (add) rows to a database table. Insert can be used in several ways: • To insert a single complete row • To insert a single partial row • To insert the results of a query You’ll now look at each of these.

INSERT and System Security Use of the INSERT statement might require special security privileges in clientserver DBMSs. Before you attempt to use INSERT, make sure you have adequate security privileges to do so.

Inserting Complete Rows The simplest way to insert data into a table is to use the basic INSERT syntax, which requires that you specify the table name and the values to be inserted into the new row. Here is an example of this:

INPUT

INSERT INTO Customers VALUES(‘1000000006’, ‘Toy Land’, ‘123 Any Street’, ‘New York’, ‘NY’, ‘11111’, ‘USA’, NULL, NULL);

The above example inserts a new customer into the Customers table. The data to be stored in each table column is specified in the VALUES clause, and a value must be provided for every column. If a column has no value (for example, the cust_contact and cust_email columns above), the NULL value should be used (assuming the table allows no value to be specified for that column). The columns must be populated in the order in which they appear in the table definition.

ANALYSIS

The INTO Keyword In some SQL implementations, the INTO keyword following INSERT is optional. However, it is good practice to provide this keyword even if it is not needed. Doing so will ensure that your SQL code is portable between DBMSs.

Although this syntax is indeed simple, it is not at all safe and should generally be avoided at all costs. The above SQL statement is highly dependent on the order in which the columns are defined in the table. It also depends on information about that order being readily available. Even if it is available, there is no guarantee that the columns will be in the exact same order the next time the table is reconstructed. Therefore, writing SQL statements that depend on specific column ordering is very unsafe. If you do so, something will inevitably break at some point. The safer (and unfortunately more cumbersome) way to write the INSERT statement is as follows:

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INPUT

INSERT INTO Customers(cust_id, cust_name, cust_address, cust_city, cust_state, cust_zip, cust_country, cust_contact, cust_email) VALUES(‘1000000006’, ‘Toy Land’, ‘123 Any Street’, ‘New York’, ‘NY’, ‘11111’, ‘USA’, NULL, NULL);

This example does the exact same thing as the previous INSERT statement, but this time the column names are explicitly stated in parentheses after the table name. When the row is inserted the DBMS will match each item in the columns list with the appropriate value in the VALUES list. The first entry in VALUES corresponds to the first specified column name. The second value corresponds to the second column name, and so on.

ANALYSIS

Because column names are provided, the VALUES must match the specified column names in the order in which they are specified, and not necessarily in the order that the columns appear in the actual table. The advantage of this is that, even if the table layout changes, the INSERT statement will still work correctly. The following INSERT statement populates all the row columns (just as before), but it does so in a different order. Because the column names are specified, the insertion will work correctly:

INPUT

INSERT INTO Customers(cust_id, cust_contact, cust_email, cust_name, cust_address, cust_city,

Inserting Data

cust_state, cust_zip) VALUES(‘1000000006’, NULL, NULL, ‘Toy Land’, ‘123 Any Street’, ‘New York’, ‘NY’, ‘11111’);

Always Use a Columns List As a rule, never use INSERT without explicitly specifying the column list. This will greatly increase the probability that your SQL will continue to function in the event that table changes occur.

Use VALUES Carefully Regardless of the INSERT syntax being used, the correct number of VALUES must be specified. If no column names are provided, a value must be present for every table column. If columns names are provided, a value must be present for each listed column. If none is present, an error message will be generated, and the row will not be inserted.

Inserting Partial Rows As I just explained, the recommended way to use INSERT is to explicitly specify table column names. Using this syntax, you can also omit columns. This means you only provide values for some columns, but not for others. Look at the following example:

INPUT

INSERT INTO Customers(cust_id, cust_name, cust_address, cust_city,

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cust_state, cust_zip, cust_country) VALUES(‘1000000006’, ‘Toy Land’, ‘123 Any Street’, ‘New York’, ‘NY’, ‘11111’, ‘USA’);

In the examples given earlier in this lesson, values were not provided for two of the columns, cust_contact and cust_email. This means there is no reason to include those columns in the INSERT statement. This INSERT statement, therefore, omits the two columns and the two corresponding values.

ANALYSIS

Omitting Columns You may omit columns from an INSERT operation if the table definition so allows. One of the following conditions must exist: • The column is defined as allowing NULL values (no value at all). • A default value is specified in the table definition. This means the default value will be used if no value is specified. If you omit a value from a table that does not allow NULL values and does not have a default, the DBMS

will generate an error message, and the row will not be inserted.

Inserting Retrieved Data INSERT is usually used to add a row to a table using specified values. There is another form of INSERT that can be used to insert the result of a SELECT statement into a table. This is known as INSERT SELECT, and, as its name suggests, it is made up of an INSERT statement and a SELECT statement.

Inserting Data

Suppose you want to merge a list of customers from another table into your Customers table. Instead of reading one row at a time and inserting it with INSERT, you can do the following:

Instructions Needed for the Next Example The following example imports data from a table named CustNew into the Customers table. To try this example, create and populate the CustNew table first. The format of the CustNew table should be the same as the Customers table described in Appendix A. When populating CustNew, be sure not to use cust_id values that were already used in Customers (the subsequent INSERT operation will fail if primary key values are duplicated).

INPUT

INSERT INTO Customers(cust_id, cust_contact, cust_email, cust_name, cust_address, cust_city, cust_state, cust_zip, cust_country) SELECT cust_id, cust_contact, cust_email, cust_name, cust_address, cust_city, cust_state, cust_zip, cust_country FROM CustNew;

This example uses INSERT SELECT to import all the data from CustNew into Customers. Instead of listing the VALUES to be inserted, the SELECT statement retrieves them from CustNew. Each column in the SELECT corresponds to a column in the specified columns

ANALYSIS

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list. How many rows will this statement insert? That depends on how many rows are in the CustNew table. If the table is empty, no rows will be inserted (and no error will be generated because the operation is still valid). If the table does, in fact, contain data, all that data is inserted into Customers.

Column Names in INSERT SELECT This example uses the same column names in both the INSERT and SELECT statements for simplicity’s sake. But there is no requirement that the column names match. In fact, the DBMS does not even pay attention to the column names returned by the SELECT. Rather, the column position is used, so the first column in the SELECT (regardless of its name) will be used to populate the first specified table column, and so on.

The SELECT statement used in an INSERT clause to filter the data to be inserted.

SELECT

can include a WHERE

Inserting Multiple Rows INSERT usually inserts only a single row. To insert multiple rows you must execute multiple INSERT statements. The exception to this rule is INSERT SELECT, which can be used to insert multiple rows with a single statement—whatever the SELECT statement returns will be inserted by the INSERT.

Copying from One Table to Another There is another form of data insertion that does not use the INSERT statement at all. To copy the contents of a table into a brand new table (one that is created on-the-fly) you can use the SELECT INTO statement.

Inserting Data

Not Supported by DB2 DB2 does not support the use of SELECT INTO as described here.

Unlike INSERT SELECT, which appends data to an existing table, SELECT INTO copies data into a new table (and depending on the DBMS being used, can overwrite the table if it already exists).

INSERT SELECT versus SELECT INTO One way to explain the differences between SELECT INTO and INSERT SELECT is that the former exports data while the latter imports data.

The following example demonstrates the use of SELECT

INPUT

INTO:

SELECT * INTO CustCopy FROM Customers;

This SELECT statement creates a new table named CustCopy and copies the entire contents of the Customers table into it. Because SELECT * was used, every column in the Customers table will be created (and populated) in the CustCopy table. To copy only a subset of the available columns, explicit column names can be specified instead of the * wildcard character.

ANALYSIS

MySQL and Oracle use a slightly different syntax:

INPUT

CREATE TABLE CustCopy AS SELECT * FROM Customers;

Here are some things to consider when using SELECT

INTO:

• Any SELECT options and clauses may be used including WHERE and GROUP BY.

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• Joins may be used to insert data from multiple tables. • Data may only be inserted into a single table regardless of how many tables the data was retrieved from.

Making Copies of Tables SELECT INTO is a great way to make copies of tables before experimenting with new SQL statements. By making a copy first, you’ll be able to test your SQL on that copy instead of on live data.

More Examples Looking for more examples of INSERT usage? See the example table population scripts described in Appendix A, “Sample Table Scripts.”

Summary In this lesson, you learned how to INSERT rows into a database table. You learned several ways to use INSERT, and why explicit column specification is preferred. You also learned how to use INSERT SELECT to import rows from another table, and how to use SELECT INTO to export rows to a new table. In the next lesson, you’ll learn how to use UPDATE and DELETE to further manipulate table data.

LESSON 16

Updating and Deleting Data In this lesson, you will learn how to use the UPDATE and DELETE statements to enable you to further manipulate your table data.

Updating Data To update (modify) data in a table the UPDATE statement is used. UPDATE can be used in two ways: • To update specific rows in a table • To update all rows in a table Let’s take a look at each of these uses.

Don’t Omit the WHERE Clause Special care must be exercised when using UPDATE, because it is all too easy to mistakenly update every row in your table. Please read this entire section on UPDATE before using this statement.

UPDATE and Security Use of the UPDATE statement might require special security privileges in client-server DBMSs. Before you attempt to use UPDATE, make sure you have adequate security privileges to do so.

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The UPDATE statement is very easy to use—some would say too easy. The basic format of an UPDATE statement is made up of three parts: • The table to be updated • The column names and their new values • The filter condition that determines which rows should be updated Let’s take a look at a simple example. Customer 1000000005 now has an email address, and so his record needs updating. The following statement performs this update:

INPUT

UPDATE Customers SET cust_email = ‘[email protected]’ WHERE cust_id = ‘1000000005’;

The UPDATE statement always begins with the name of the table being updated. In this example, it is the Customers table. The SET command is then used to assign the new value to a column. As used here, the SET clause sets the cust_email column to the specified value: SET cust_email = ‘[email protected]’

The UPDATE statement finishes with a WHERE clause that tells the DBMS which row to update. Without a WHERE clause, the DBMS would update all the rows in the Customers table with this new email address—definitely not the desired effect. Updating multiple columns requires a slightly different syntax:

INPUT

UPDATE Customers SET cust_contact = ‘Sam Roberts’, cust_email = ‘[email protected]’ WHERE cust_id = ‘1000000006’;

When updating multiple columns, only a single SET command is used, and each column = value pair is separated by a comma. (No comma is specified after the last column.) In this example, columns cust_contact and cust_email will both be updated for customer 1000000006.

Updating and Deleting Data

Using Subqueries in an UPDATE Statement Subqueries may be used in UPDATE statements, enabling you to update columns with data retrieved with a SELECT statement. Refer back to Lesson 11, “Working with Subqueries,” for more information on subqueries and their uses.

The FROM Keyword Some SQL implementations support a FROM clause in the UPDATE statement that can be used to update the rows in one table with data from another table. Refer to your DBMS documentation to see if it supports this feature.

To delete a column’s value, you can set it to NULL (assuming the table is defined to allow NULL values). You can do this as follows:

INPUT

UPDATE Customers SET cust_email = NULL WHERE cust_id = ‘1000000005’;

Here the NULL keyword is used to save no value to the cust_email column.

Deleting Data To delete (remove) data from a table, the DELETE statement is used. DELETE can be used in two ways: • To delete specific rows from a table • To delete all rows from a table You’ll now take a look at each of these.

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Don’t Omit the WHERE Clause Special care must be exercised when using DELETE because it is all too easy to mistakenly delete every row from your table. Please read this entire section on DELETE before using this statement.

DELETE and Security Use of the DELETE statement might require special security privileges in client-server DBMSs. Before you attempt to use DELETE, make sure you have adequate security privileges to do so.

I already stated that UPDATE is very easy to use. The good (and bad) news is that DELETE is even easier to use. The following statement deletes a single row from the Customers table:

INPUT

DELETE FROM Customers WHERE cust_id = ‘1000000006’;

This statement should be self-explanatory. DELETE FROM requires that you specify the name of the table from which the data is to be deleted. The WHERE clause filters which rows are to be deleted. In this example, only customer 1000000006 will be deleted. If the WHERE clause were omitted, this statement would have deleted every customer in the table.

The FROM Keyword In some SQL implementations, the FROM keyword following DELETE is optional. However, it is good practice to always provide this keyword, even if it is not needed. Doing this will ensure that your SQL code is portable between DBMSs

takes no column names or wildcard characters. DELETE deletes entire rows, not columns. To delete specific columns use an UPDATE statement.

DELETE

Updating and Deleting Data

Table Contents, Not Tables The DELETE statement deletes rows from tables, even all rows from tables. But DELETE never deletes the table itself.

Faster Deletes If you really do want to delete all rows from a table, don’t use DELETE. Instead, use the TRUNCATE TABLE statement which accomplished the same thing but does it much quicker (because data changes are not logged).

Guidelines for Updating and Deleting Data The UPDATE and DELETE statements used in the previous section all have WHERE clauses, and there is a very good reason for this. If you omit the WHERE clause, the UPDATE or DELETE will be applied to every row in the table. In other words, if you execute an UPDATE without a WHERE clause, every row in the table will be updated with the new values. Similarly if you execute DELETE without a WHERE clause, all the contents of the table will be deleted. Here are some best practices that many SQL programmers follow: • Never execute an UPDATE or a DELETE without a WHERE clause unless you really do intend to update and delete every row. • Make sure every table has a primary key (refer back to Lesson 12, “Joining Tables,” if you have forgotten what this is), and use it as the WHERE clause whenever possible. (You may specify individual primary keys, multiple values, or value ranges.) • Before you use a WHERE clause with an UPDATE or a DELETE, first test it with a SELECT to make sure it is filtering the right records—it is far too easy to write incorrect WHERE clauses.

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• Use database enforced referential integrity (refer back to Lesson 12 for this one, too) so that the DBMS will not allow the deletion of rows that have data in other tables related to them. • Some DBMSs allow database administrators to impose restrictions that prevent the execution of UPDATE or DELETE without a WHERE clause. If your DBMS supports this feature, consider using it.

Use With Caution The bottom line is that SQL has no Undo button. Be very careful using UPDATE and DELETE, or you’ll find yourself updating and deleting the wrong data.

Summary In this lesson, you learned how to use the UPDATE and DELETE statements to manipulate the data in your tables. You learned the syntax for each of these statements, as well as the inherent dangers they expose. You also learned why WHERE clauses are so important in UPDATE and DELETE statements, and you were given guidelines that should be followed to help ensure that data does not get damaged inadvertently.

LESSON 17

Creating and Manipulating Tables In this lesson you’ll learn the basics of table creation, alteration, and deletion.

Creating Tables SQL is not used just for table data manipulation. Rather, SQL can be used to perform all database and table operations, including the creation and manipulation of tables themselves. There are generally two ways to create database tables: • Most DBMSs come with an administration tool that can be used to create and manage database tables interactively. • Tables may also be manipulated directly with SQL statements. To create tables programmatically, the CREATE TABLE SQL statement is used. It is worth noting that when you use interactive tools, you are actually using SQL statements. Instead of your writing these statements, however, the interface generates and executes the SQL seamlessly for you (the same is true for changes to existing tables).

Syntax Differences The exact syntax of the CREATE TABLE statement can vary from one SQL implementation to another. Be sure to refer to your DBMS documentation for more information on exactly what syntax and features it supports.

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Complete coverage of all the options available when creating tables is beyond the scope of this lesson, but here are the basics. I’d recommend that you review your DBMS documentation for more information and specifics.

DBMS Specific Examples For examples of DBMS specific CREATE TABLE statements, see the example table creation scripts described in Appendix A, “Sample Table Scripts.”

Basic Table Creation To create a table using CREATE information:

TABLE,

you must specify the following

• The name of the new table specified after the keywords CREATE TABLE. • The name and definition of the table columns separated by commas. • Some DBMSs require that you also specify the table location. The following SQL statement creates the Products table used throughout this book:

INPUT

CREATE TABLE Products ( prod_id CHAR(10) vend_id CHAR(10) prod_name CHAR(254) prod_price DECIMAL(8,2) prod_desc VARCHAR(1000) );

NOT NULL, NOT NULL, NOT NULL, NOT NULL, NULL

As you can see in the above statement, the table name is specified immediately following the CREATE TABLE keywords. The actual table definition (all the columns) is enclosed within parentheses. The columns themselves are separated by commas. This particular table is made up of five columns. Each column definition starts

ANALYSIS

Creating and Manipulating Tables

with the column name (which must be unique within the table), followed by the column’s datatype. (Refer to Lesson 1, “Understanding SQL,” for an explanation of datatypes. In addition, Appendix D, “Using SQL Datatypes,” lists commonly used datatypes and their compatibility.) The entire statement is terminated with a semicolon after the closing parenthesis. I mentioned earlier that CREATE TABLE syntax varies greatly from one DBMS to another, and the simple script just seen demonstrates this. While the statement will work as is on Oracle, PostgreSQL, SQL Server, and Sybase, for MySQL the varchar must be replaced with text, and for DB2 the NULL must be removed from the final column. This is why I had to create a different SQL table creation script for each DBMS (as explained in Appendix A).

Statement Formatting As you will recall, whitespace is ignored in SQL statements. Statements can be typed on one long line or broken up over many lines. It makes no difference at all. This enables you to format your SQL as best suits you. The preceding CREATE TABLE statement is a good example of SQL statement formatting—the code is specified over multiple lines, with the column definitions indented for easier reading and editing. Formatting your SQL in this way is entirely optional, but highly recommended.

Replacing Existing Tables When you create a new table, the table name specified must not exist or you’ll generate an error. To prevent accidental overwriting, SQL requires that you first manually remove a table (see later sections for details) and then recreate it, rather than just overwriting it.

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Working with NULL Values Back in Lesson 4, “Filtering Data,” you learned that NULL values are no values or the lack of a value. A column that allows NULL values also allows rows to be inserted with no value at all in that column. A column that does not allow NULL values does not accept rows with no value—in other words, that column will always be required when rows are inserted or updated. Every table column is either a NULL column or a NOT NULL column, and that state is specified in the table definition at creation time. Take a look at the following example:

INPUT

CREATE TABLE Orders ( order_num INTEGER order_date DATETIME cust_id CHAR(10) );

NOT NULL, NOT NULL, NOT NULL

This statement creates the Orders table used throughout this book. Orders contains three columns: order number, order date, and the customer ID. All three columns are required, and so each contains the keyword NOT NULL. This will prevent the insertion of columns with no value. If someone tries to insert no value, an error will be returned, and the insertion will fail.

ANALYSIS

This next example creates a table with a mixture of NULL and NOT columns:

INPUT

ANALYSIS

CREATE TABLE Vendors ( vend_id CHAR(10) vend_name CHAR(50) vend_address CHAR(50) vend_city CHAR(50) vend_state CHAR(5) vend_zip CHAR(10) vend_country CHAR(50) );

NULL

NOT NULL, NOT NULL, , , , ,

This statement creates the Vendors table used throughout this book. The vendor ID and vendor name columns are both

Creating and Manipulating Tables

required, and are, therefore, specified as NOT NULL. The five remaining columns all allow NULL values, and so NOT NULL is not specified. NULL is the default setting, so if NOT NULL is not specified NULL is assumed.

Specifying NULL Most DBMSs treat the absence of NOT NULL to mean NULL. However, not all do. DB2 requires the keyword NULL and will generate an error if it is not specified. Refer to your DBMS documentation for complete syntax information.

Primary Keys and NULL Values Back in Lesson 1, you learned that primary keys are columns whose values uniquely identify every row in a table. Only columns that do not allow NULL values can be used in primary keys. Columns that allow no value at all cannot be used as unique identifiers.

Understanding NULL Don’t confuse NULL values with empty strings. A NULL value is the lack of a value; it is not an empty string. If you were to specify ‘’ (two single quotes with nothing in between them), that would be allowed in a NOT NULL column. An empty string is a valid value; it is not no value. NULL values are specified with the keyword NULL, not with an empty string.

Specifying Default Values SQL enables you to specify default values to be used if no value is specified when a row is inserted. Default values are specified using the DEFAULT keyword in the column definitions in the CREATE TABLE statement.

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Look at the following example:

INPUT CREATE TABLE OrderItems ( order_num INTEGER order_item INTEGER prod_id CHAR(10) quantity INTEGER item_price DECIMAL(8,2) );

NOT NOT NOT NOT NOT

NULL, NULL, NULL, NULL NULL

DEFAULT 1,

This statement creates the OrderItems table that contains the individual items that make up an order. (The order itself is stored in the Orders table.) The quantity column contains the quantity for each item in an order. In this example, adding the text DEFAULT 1 to the column description instructs the DBMS to use a quantity of 1 if no quantity is specified.

ANALYSIS

Default values are often used to store values in date or time stamp columns. For example, the system date can be used as a default date by specifying the function or variable used to refer to the system date. For example, MySQL users might specify DEFAULT CURRENT_DATE(), while Oracle users might specify DEFAULT SYSDATE, and SQL Server users might specify DEFAULT GETDATE(). Unfortunately, the command used to obtain the system date is different in just about every DBMS. Table 17.1 lists the syntax for some DBMSs. If yours is not listed here consult your DBMSs documentation.

TABLE 17.1

Obtaining The System Date

DBMS

Function/Variable

Access

NOW()

DB2

CURRENT_DATE

MySQL

CURRENT_DATE()

Oracle

SYSDATE

PostgreSQL

CURRENT_DATE

Creating and Manipulating Tables

DBMS

Function/Variable

SQL Server

GETDATE()

Sybase

GETDATE()

Using DEFAULT Instead of NULL Values Many database developers use DEFAULT values instead of NULL columns, especially in columns that will be used in calculations or data groupings.

Updating Tables To update table definitions, the ALTER TABLE statement is used. Although all DBMSs support ALTER TABLE, what they allow you to alter varies dramatically from one to another. Here are some points to consider when using ALTER TABLE: • Ideally, tables should never be altered after they contain data. You should spend sufficient time anticipating future needs during the table design process so that extensive changes are not required later on. • All DBMSs allow you to add columns to existing tables, although some restrict the datatypes that may be added (as well as NULL and DEFAULT usage). • Many DBMSs do not allow you to remove or change columns in a table. • Most DBMSs allow you to rename columns. • Many DBMSs restrict the kinds of changes you can make on columns that are populated and enforce fewer restrictions on unpopulated columns. As you can see, making changes to existing tables is neither simple nor consistent. Be sure to refer to your own DBMS documentation to determine exactly what you can alter.

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To change a table using ALTER information:

TABLE,

you must specify the following

• The name of the table to be altered after the keywords ALTER TABLE. (The table must exist or an error will be generated.) • The list of changes to be made. Because adding columns to an existing table is about the only operation supported by all DBMSs, I’ll use that for an example:

INPUT ANALYSIS

ALTER TABLE Vendors ADD vend_phone CHAR(20);

This statement adds a column named vend_phone to the Vendors table. The datatype must be specified.

Other alter operations, for example, changing or dropping columns, or adding constraints or keys, use a similar syntax. (Note that the following example will not work with all DBMSs):

INPUT

ALTER TABLE Vendors DROP COLUMN vend_phone;

Complex table structure changes usually require a manual move process involving these steps: • Create a new table with the new column layout. • Use the INSERT SELECT statement (see Lesson 15, “Inserting Data,” for details of this statement) to copy the data from the old table to the new table. Use conversion functions and calculated fields, if needed. • Verify that the new table contains the desired data. • Rename the old table (or delete it, if you are really brave). • Rename the new table with the name previously used by the old table. • Recreate any triggers, stored procedures, indexes, and foreign keys as needed.

Creating and Manipulating Tables

Use ALTER TABLE Carefully Use ALTER TABLE with extreme caution, and be sure you have a complete set of backups (both schema and data) before proceeding. Database table changes cannot be undone—and if you add columns you don’t need, you might not be able to remove them. Similarly, if you drop a column that you do need, you might lose all the data in that column.

Deleting Tables Deleting tables (actually removing the entire table, not just the contents) is very easy—arguably too easy. Tables are deleted using the DROP TABLE statement:

INPUT

DROP TABLE CustCopy;

This statement deletes the CustCopy table. (You created that one in Lesson 15.) There is no confirmation, nor is there an undo—executing the statement will permanently remove the table.

ANALYSIS

Using Relational Rules to Prevent Accidental Deletion Many DBMSs allow you to enforce rules that prevent the dropping of tables that are related to other tables. When these rules are enforced, if you issue a DROP TABLE statement against a table that is part of a relationship, the DBMS blocks the operation until the relationship was removed. It is a good idea to enable these options, if available, to prevent the accidental dropping of needed tables.

Renaming Tables Table renaming is supported differently by each DBMS. There is no hard and fast standard for this operation. DB2, MySQL, Oracle, and

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PostgreSQL users can use the RENAME statement. SQL Server and Sybase users can use the supplied sp_rename stored procedure. The basic syntax for all rename operations requires that you specify the old name and a new name. However, there are DBMS implementation differences. Refer to your own DBMS documentation for details on supported syntax.

Summary In this lesson, you learned several new SQL statements. CREATE TABLE is used to create new tables, ALTER TABLE is used to change table columns (or other objects like constraints or indexes), and DROP TABLE is used to completely delete a table. These statements should be used with extreme caution, and only after backups have been made. As the exact syntax of each of these statements varies from one DBMS to another, you should consult your own DBMS documentation for more information.

LESSON 18

Using Views In this lesson you’ll learn exactly what views are, how they work, and when they should be used. You’ll also see how views can be used to simplify some of the SQL operations performed in earlier lessons.

Understanding Views Views are virtual tables. Unlike tables that contain data, views simply contain queries that dynamically retrieve data when used.

MySQL Support As this book goes to press, MySQL still does not support views (support for views is planned for MySQL 5). As such, the examples in this lesson will not work with MySQL at this time.

The best way to understand views is to look at an example. Back in Lesson 12, “Joining Tables,” you used the following SELECT statement to retrieve data from three tables:

INPUT

SELECT cust_name, cust_contact FROM Customers, Orders, OrderItems WHERE Customers.cust_id = Orders.cust_id AND OrderItems.order_num = Orders.order_num AND prod_id = ‘RGAN01’;

That query was used to retrieve the customers who had ordered a specific product. Anyone needing this data would have to understand the table structure, as well as how to create the query and join the tables. To retrieve the same data for another product (or for multiple products), the last WHERE clause would have to be modified.

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Now imagine that you could wrap that entire query in a virtual table called ProductCustomers. You could then simply do the following to retrieve the same data:

INPUT

SELECT cust_name, cust_contact FROM ProductCustomers WHERE prod_id = ‘RGAN01’;

This is where views come into play. ProductCustomers is a view, and as a view, it does not contain any columns or data. Instead it contains a query—the same query used above to join the tables properly.

DBMS Consistency You’ll be relieved to know that view creation syntax is supported pretty consistently by all the major DBMSs.

Why Use Views You’ve already seen one use for views. Here are some other common uses: • To reuse SQL statements. • To simplify complex SQL operations. After the query is written, it can be reused easily, without having to know the details of the underlying query itself. • To expose parts of a table instead of complete tables. • To secure data. Users can be given access to specific subsets of tables instead of to entire tables. • To change data formatting and representation. Views can return data formatted and presented differently from their underlying tables. For the most part, after views are created, they can be used in the same way as tables. You can perform SELECT operations, filter and sort data, join views to other views or tables, and possibly even add and update data. (There are some restrictions on this last item. More on that in a moment.)

Using Views

The important thing to remember is views are just that, views into data stored elsewhere. Views contain no data themselves, so the data they return is retrieved from other tables. When data is added or changed in those tables, the views will return that changed data.

Performance Issues Because views contain no data, any retrieval needed to execute a query must be processed every time the view is used. If you create complex views with multiple joins and filters, or if you nest views, you may find that performance is dramatically degraded. Be sure you test execution before deploying applications that use views extensively.

View Rules and Restrictions Before you create views yourself, there are some restrictions of which you should be aware. Unfortunately, the restrictions tend to be very DBMS specific, so check your own DBMS documentation before proceeding. Here are some of the most common rules and restrictions governing view creation and usage: • Like tables, views must be uniquely named. (They cannot be named with the name of any other table or view). • There is no limit to the number of views that can be created. • To create views, you must have security access. This is usually granted by the database administrator. • Views can be nested; that is, a view may be built using a query that retrieves data from another view. The exact number of nested levels allowed varies from DBMS to DBMS. (Nesting views might seriously degrade query performance, so test this thoroughly before using it in production environments.) • Many DBMSs prohibit the use of the ORDER queries.

BY

clause in view

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• Some DBMSs require that every column returned be named— this will require the use of aliases if columns are calculated fields. (See Lesson 7, “Creating Calculated Fields,” for more information on column aliases.) • Views cannot be indexed, nor can they have triggers or default values associated with them. • Some DBMSs treat views as read-only queries—meaning you can retrieve data from views but not write data back to the underlying tables. Refer to your DBMS documentation for details. • Some DBMSs allow you to create views that do not allow rows to be inserted or updated if that insertion or update will cause that row to no longer be part of the view. For example, if you have a view that retrieves only customers with email addresses, updating a customer to remove his email address would make that customer fall out of the view. This is the default behavior and is allowed, but depending on your DBMS you might be able to prevent this from occurring.

Refer to Your DBMS Documentation That’s a long list of rules, and your own DBMS documentation will likely contain additional rules, too. It is worth taking the time to understand what restrictions you must adhere to before creating views.

Creating Views So now that you know what views are (and the rules and restrictions that govern them), let’s look at view creation. Views are created using the CREATE VIEW statement. Like CREATE TABLE, CREATE VIEW can only be used to create a view that does not exist.

Using Views

To remove a view, the DROP statement is used. The syntax is simply DROP VIEW viewname;. To overwrite (or update) a view you must first DROP it and then recreate it.

Using Views to Simplify Complex Joins One of the most common uses of views is to hide complex SQL, and this often involves joins. Look at the following statement:

INPUT

CREATE VIEW ProductCustomers AS SELECT cust_name, cust_contact, prod_id FROM Customers, Orders, OrderItems WHERE Customers.cust_id = Orders.cust_id AND OrderItems.order_num = Orders.order_num;

This statement creates a view named ProductCustomers, which joins three tables to return a list of all customers who have ordered any product. If you were to SELECT * FROM ProductCustomers, you’d list every customer who ordered anything.

ANALYSIS

CREATE VIEW and SQL Server

Unlike most SQL statements, Microsoft SQL Server does not support the use of a semicolon after a CREATE VIEW statement.

To retrieve a list of customers who ordered product RGAN01 you can do the following:

INPUT OUTPUT

SELECT cust_name, cust_contact FROM ProductCustomers WHERE prod_id = ‘RGAN01’; cust_name ------------------Fun4All The Toy Store

cust_contact -----------------Denise L. Stephens Kim Howard

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This statement retrieves specific data from the view by issuing a WHERE clause. When the DBMS processes the request, it adds the specified WHERE clause to any existing WHERE clauses in the view query so that the data is filtered correctly.

ANALYSIS

As you can see, views can greatly simplify the use of complex SQL statements. Using views, you can write the underlying SQL once and then reuse it as needed.

Creating Reusable Views It is a good idea to create views that are not tied to specific data. For example, the view created above returns customers for all products, not just product RGAN01 (for which the view was first created). Expanding the scope of the view enables it to be reused, making it even more useful. It also eliminates the need for you to create and maintain multiple similar views.

Using Views to Reformat Retrieved Data As mentioned above, another common use of views is for reformatting retrieved data. The following SELECT statement (from Lesson 7, “Creating Calculated Fields”) returns vendor name and location in a single combined calculated column:

INPUT

OUTPUT

SELECT RTRIM(vend_name) + ‘ (‘ + RTRIM(vend_country) ➥+ ‘)’ AS vend_title FROM Vendors ORDER BY vend_name; vend_title ---------------------------------------------------Bear Emporium (USA) Bears R Us (USA) Doll House Inc. (USA) Fun and Games (England) Furball Inc. (USA) Jouets et ours (France)

Using Views

The following is the same statement, but using the || syntax (as explained back in Lesson 7):

INPUT

OUTPUT

SELECT RTRIM(vend_name) || ‘ (‘ || ➥RTRIM(vend_country) || ‘)’ AS vend_title FROM Vendors ORDER BY vend_name; vend_title ---------------------------------------------------Bear Emporium (USA) Bears R Us (USA) Doll House Inc. (USA) Fun and Games (England) Furball Inc. (USA) Jouets et ours (France)

Now suppose that you regularly needed results in this format. Rather than perform the concatenation each time it was needed, you could create a view and use that instead. To turn this statement into a view, you can do the following:

INPUT

CREATE VIEW VendorLocations AS SELECT RTRIM(vend_name) + ‘ (‘ + RTRIM(vend_country) ➥+ ‘)’ AS vend_title FROM Vendors;

Here’s the same statement using || syntax:

INPUT

CREATE VIEW VendorLocations AS SELECT RTRIM(vend_name) || ‘ (‘ || ➥RTRIM(vend_country) || ‘)’ AS vend_title FROM Vendors;

This statement creates a view using the exact same query as the previous SELECT statement. To retrieve the data to create all mailing labels, simply do the following:

ANALYSIS

INPUT

SELECT * FROM VendorLocations;

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OUTPUT

vend_title ---------------------------------------------------Bear Emporium (USA) Bears R Us (USA) Doll House Inc. (USA) Fun and Games (England) Furball Inc. (USA) Jouets et ours (France)

SELECT Restrictions All Apply Earlier in this lesson I stated that the syntax used to create views was rather consistent between DBMSs. So why multiple versions of statements? A view simply wraps a SELECT statement, and the syntax of that SELECT must adhere to all the rules and restrictions of the DBMS being used.

Using Views to Filter Unwanted Data Views are also useful for applying common WHERE clauses. For example, you might want to define a CustomerEMailList view so that it filters out customers without email addresses. To do this, you can use the following statement

INPUT

CREATE VIEW CustomerEMailList AS SELECT cust_id, cust_name, cust_email FROM Customers WHERE cust_email IS NOT NULL;

Obviously, when sending email to a mailing list you’d want to ignore users who have no email address. The WHERE clause here filters out those rows that have NULL values in the cust_email columns so that they’ll not be retrieved.

ANALYSIS

View CustomerEMailList can now be used like any table.

INPUT

SELECT * FROM CustomerEMailList;

Using Views

OUTPUT

cust_id ---------1000000001 1000000003 1000000004

cust_name -----------Village Toys Fun4All Fun4All

cust_email [email protected] [email protected] [email protected]

WHERE Clauses and WHERE Clauses If a WHERE clause is used when retrieving data from the view, the two sets of clauses (the one in the view and the one passed to it) will be combined automatically.

Using Views with Calculated Fields Views are exceptionally useful for simplifying the use of calculated fields. The following is a SELECT statement introduced in Lesson 7. It retrieves the order items for a specific order, calculating the expanded price for each item:

INPUT

OUTPUT

SELECT prod_id, quantity, item_price, quantity*item_price AS expanded_price FROM OrderItems WHERE order_num = 20008; prod_id ------RGAN01 BR03 BNBG01 BNBG02 BNBG03

quantity -------5 5 10 10 10

item_price ---------4.9900 11.9900 3.4900 3.4900 3.4900

expanded_price -------------24.9500 59.9500 34.9000 34.9000 34.9000

To turn this into a view, do the following:

INPUT

CREATE VIEW OrderItemsExpanded AS SELECT order_num, prod_id, quantity, item_price, quantity*item_price AS expanded_price FROM OrderItems;

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To retrieve the details for order 20008 (the output above), do the following:

INPUT SELECT * FROM OrderItemsExpanded WHERE order_num = 20008;

OUTPUT order_num --------20008 20008 20008 20008 20008

prod_id ------RGAN01 BR03 BNBG01 BNBG02 BNBG03

quantity -------5 5 10 10 10

item_price ---------4.99 11.99 3.49 3.49 3.49

expanded_price -------------24.95 59.95 34.90 34.90 34.90

As you can see, views are easy to create and even easier to use. Used correctly, views can greatly simplify complex data manipulation.

Summary Views are virtual tables. They do not contain data, but instead, they contain queries that retrieve data as needed. Views provide a level of encapsulation around SQL SELECT statements and can be used to simplify data manipulation, as well as to reformat or secure underlying data.

LESSON 19

Working with Stored Procedures In this lesson, you’ll learn what stored procedures are, why they are used, and how. You’ll also look at the basic syntax for creating and using them.

Understanding Stored Procedures Most of the SQL statements that we’ve used thus far are simple in that they use a single statement against one or more tables. Not all operations are that simple—often, multiple statements will be needed to perform a complete operation. For example, consider the following scenario: • To process an order, checks must be made to ensure that items are in stock. • If items are in stock, they need to be reserved so that they are not sold to anyone else, and the available quantity must be reduced to reflect the correct amount in stock. • Any items not in stock need to be ordered; this requires some interaction with the vendor. • The customer needs to be notified as to which items are in stock (and can be shipped immediately) and which are back ordered. This is obviously not a complete example, and it is even beyond the scope of the example tables that we have been using in this book, but it will suffice to help make a point. Performing this process requires many SQL statements against many tables. In addition, the exact SQL statements that

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need to be performed and their order are not fixed; they can (and will) vary according to which items are in stock and which are not. How would you write this code? You could write each of the SQL statements individually and execute other statements conditionally, based on the result. You’d have to do this every time this processing was needed (and in every application that needed it). You could create a stored procedure. Stored procedures are simply collections of one or more SQL statements saved for future use. You can think of them as batch files, although in truth they are more than that.

Access and MySQL Stored procedures are not supported in Access. In addition, as this book goes to press, MySQL v4.x (the current version) does not support stored procedures (support is planned for MySQL 5).

There’s a Lot More to It Stored procedures are complex, and full coverage of the subject requires more space than can be allocated here. This lesson will not teach you all you need to know about stored procedures. Rather, it is intended simply to introduce the subject so that you are familiar with what they are and what they can do. As such, the examples presented here provide syntax for Oracle and SQL Server only.

Why to Use Stored Procedures Now that you know what stored procedures are, why use them? There are lots of reasons, but here are the primary ones: • To simplify complex operations (as seen in the previous example) by encapsulating processes into a single easy-to-use unit.

Working with Stored Procedures

• To ensure data consistency by not requiring that a series of steps be created over and over. If all developers and applications use the same stored procedure, then the same code will be used by all. An extension of this is to prevent errors. The more steps that need to be performed, the more likely it is that errors will be introduced. Preventing errors ensures data consistency. • To simplify change management. If tables, column names, or business logic (or just about anything) changes, then only the stored procedure code needs to be updated, and no one else will need even to be aware that changes were made. An extension of this is security. Restricting access to underlying data via stored procedures reduces the chance of data corruption (unintentional or otherwise). • Because stored procedures are usually stored in a compiled form, the DBMS has to do less work to process the command. This results in improved performance. • There are SQL language elements and features that are available only within single requests. Stored procedures can use these to write code that is more powerful and flexible. In other words, there are three primary benefits—simplicity, security, and performance. Obviously all are extremely important. Before you run off to turn all your SQL code into stored procedures, here’s the downside: • Stored procedure syntax varies dramatically from one DBMS to the next. In fact, it is close to impossible to write truly portable stored procedures. Having said that, how the stored procedures call themselves (their names and how data is passed to them) can be kept relatively portable so that if you need to change to another DBMS at least your client application code may not need changing. • Stored procedures tend to be more complex to write than basic SQL statements, and writing them requires a greater degree of skill and experience. As a result, many database administrators restrict stored procedure creation rights as a security measure (primarily due to the previous bullet item).

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Nonetheless, stored procedures are very useful and should be used. In fact, most DBMSs come with all sorts of stored procedures that are used for database and table management. Refer to your DBMS documentation for more information on these.

Can’t Write Them? You Can Still Use Them Most DBMSs distinguish the security and access needed to write stored procedures from the security and access needed to execute them. This is a good thing; even if you can’t (or don’t want to) write your own stored procedures, you can still execute them when appropriate.

Executing Stored Procedures Stored procedures are executed far more often than they are written, so we’ll start there. The SQL statement to execute a stored procedure is simply EXECUTE. EXECUTE takes the name of the stored procedure and any parameters that need to be passed to it. Take a look at this example:

INPUT

EXECUTE AddNewProduct(‘JTS01’, ‘Stuffed Eiffel Tower’, 6.49, ‘Plush stuffed toy with the ➥text La Tour Eiffel in red white and blue’)

Here a stored procedure named AddNewProduct is executed; it adds a new product to the Products table. AddNewProduct takes four parameters—the vendor ID (the primary key from the Vendors table), the product name, price, and description. These four parameters match four expected variables within the stored procedure (defined as part of the stored procedure itself). The stored procedure adds a new row to the Products table and assigns these passed attributes to the appropriate columns.

ANALYSIS

In the Products table you’ll notice that there is another column that needs a value: the prod_id column, which is the table’s primary key. Why was

Working with Stored Procedures

this value not passed as an attribute to the stored procedure? To ensure that IDs are generated properly, it is safer to have that process automated (and not rely on end users). That is why a stored procedure is used in this example. This is what this stored procedure does: • It validates the passed data, ensuring that all four parameters have values. • It generates a unique ID to be used as the primary key. • It inserts the new product into the Products table, storing the generated primary key and passed data in the appropriate columns. This is the basic form of stored procedure execution. Depending on the DBMS used, other execution options include the following: • Optional parameters, with default values assumed if a parameter is not provided • Out-of-order parameters, specified in parameter=value pairs • Output parameters, allowing the stored procedure to update a parameter for use in the executing application • Data retrieved by a SELECT statement • Return codes, enabling the stored procedure to return a value to the executing application

Creating Stored Procedures As already explained, writing a stored procedure is not trivial. To give you a taste for what is involved, let’s look at a simple example—a stored procedure that counts the number of customers in a mailing list who have email addresses. Here is the Oracle version:

INPUT

CREATE PROCEDURE MailingListCount (ListCount OUT NUMBER) IS BEGIN

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SELECT * FROM Customers WHERE NOT cust_email IS NULL; ListCount := SQL%ROWCOUNT; END;

This stored procedure takes a single parameter named ListCount. Instead of passing a value to the stored procedure, this parameter passes a value back from it. The keyword OUT is used to specify this behavior. Oracle supports parameters of types IN (those passed to stored procedures), OUT (those passed from stored procedures, as we’ve used here), and INOUT (those used to pass parameters to and from stored procedures). The stored procedure code itself is enclosed within BEGIN and END statements, and here a simple SELECT is performed to retrieve the customers with email addresses. Then ListCount (the output parameter passed) is set with the number of rows that were retrieved.

ANALYSIS

Here’s the Microsoft SQL Server version:

INPUT

CREATE PROCEDURE MailingListCount AS DECLARE @cnt INTEGER SELECT @cnt = COUNT(*) FROM Customers WHERE NOT cust_email IS NULL; RETURN @cnt;

This stored procedure takes no parameters at all. The calling application retrieves the value by using SQL Server’s return code support. Here a local variable named @cnt is declared using the DECLARE statement (all local variables in SQL Server are named starting with a @). This variable is then used in the SELECT statement so that it contains the value returned by the COUNT() function. Finally, the RETURN statement is used to return the count to the calling application as RETURN @cnt.

ANALYSIS

Here’s another example, this time to insert a new order in the Orders table. This is a SQL Server–only example, but it demonstrates some useful stored procedure uses and techniques:

INPUT

CREATE PROCEDURE NewOrder @cust_id CHAR(10) AS -- Declare variable for order number DECLARE @order_num INTEGER -- Get current highest order number

Working with Stored Procedures

SELECT @order_num=MAX(order_num) FROM Orders -- Determine next order number SELECT @order_num=@order_num+1 -- Insert new order INSERT INTO Orders(order_num, order_date, cust_id) VALUES(@order_num, GETDATE(), @cust_id) -- Return order number RETURN @order_num;

This stored procedure creates a new order in the Orders table. It takes a single parameter, the ID of the customer placing the order. The other two table columns, the order number and order date, are generated automatically within the stored procedure itself. The code first declares a local variable to store the order number. Next, the current highest order number is retrieved (using a MAX() function) and incremented (using a SELECT statement). Then the order is inserted with an INSERT statement using the newly generated order number, the current system date (retrieved using the GETDATE() function), and the passed customer ID. Finally, the order number (which is needed to process order items) is returned as RETURN @order_num. Notice that the code is commented—this should always be done when writing stored procedures.

ANALYSIS

Comment Your Code All code should be commented, and stored procedures are no different. Adding comments will not affect performance at all, so there is no downside here (other than the time it takes to write them). The benefits are numerous and include making it easier for others (and yourself) to understand the code and safer to make changes at a later date. The standard way to comment code is to precede it by -- (two hyphens). Some DBMSs support alternate comment syntax, but all support –- and so you are best off using that.

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Here’s a quite different version of the same SQL Server code:

INPUT

CREATE PROCEDURE NewOrder @cust_id CHAR(10) AS -- Insert new order INSERT INTO Orders(cust_id) VALUES(@cust_id) -- Return order number SELECT order_num = @@IDENTITY;

This stored procedure also creates a new order in the Orders table. This time the DBMS itself generates the order number. Most DBMSs support this type of functionality; SQL Server refers to these auto-incrementing columns as Identity fields (other DBMSs use names such as Auto Number or Sequences). Again, a single parameter is passed: the customer ID of the customer placing the order. The order number and order date are not specified at all—the DBMS uses a default value for the date (the GETDATE() function), and the order number is generated automatically. How can you find out what the generated ID is? SQL Server makes that available in the global variable @@IDENTITY, which is returned to the calling application (this time using a SELECT statement).

ANALYSIS

As you can see, with stored procedures there are often many different ways to accomplish the same task. The method you choose will often be dictated by the features of the DBMS you are using.

Summary In this lesson, you learned what stored procedures are and why they are used. You also learned the basics of stored procedure execution and creation syntax, and you saw some of the ways these can be used. Your own DBMS probably offers some form of these functions, as well as others not mentioned here. Refer to your DBMS documentation for more details.

LESSON 20

Managing Transaction Processing In this lesson, you’ll learn what transactions are and how to use COMMIT and ROLLBACK statements to manage transaction processing.

Understanding Transaction Processing Transaction processing is used to maintain database integrity by ensuring that batches of SQL operations execute completely or not at all. As explained back in Lesson 12, “Joining Tables,” relational databases are designed so that data is stored in multiple tables to facilitate easier data manipulation, management, and reuse. Without going in to the hows and whys of relational database design, take it as a given that well-designed database schemas are relational to some degree. The Orders tables that you’ve been using in the past 18 lessons are a good example of this. Orders are stored in two tables: Orders stores actual orders, and OrderItems stores the individual items ordered. These two tables are related to each other using unique IDs called primary keys (as discussed in Lesson 1, “Understanding SQL”). These tables, in turn, are related to other tables containing customer and product information. The process of adding an order to the system is as follows: 1. Check if the customer is already in the database. If not, add him or her. 2. Retrieve the customer’s ID.

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3. Add a row to the Orders table associating it with the customer ID. 4. Retrieve the new order ID assigned in the Orders table. 5. Add one row to the OrderItems table for each item ordered, associating it with the Orders table by the retrieved ID (and with the Products table by product ID). Now imagine that some database failure (for example, out of disk space, security restrictions, table locks) prevents this entire sequence from completing. What would happen to your data? Well, if the failure occurred after the customer was added and before the table was added, there is no real problem. It is perfectly valid to have customers without orders. When you run the sequence again, the inserted customer record will be retrieved and used. You can effectively pick up where you left off. Orders

But what if the failure occurred after the Orders row was added, but before the OrderItems rows were added? Now you’d have an empty order sitting in your database. Worse, what if the system failed during adding the OrderItems rows? Now you’d end up with a partial order in your database, but you wouldn’t know it. How do you solve this problem? That’s where Transaction Processing comes in. Transaction Processing is a mechanism used to manage sets of SQL operations that must be executed in batches so as to ensure that databases never contain the results of partial operations. With Transaction Processing, you can ensure that sets of operations are not aborted midprocessing—they either execute in their entirety or not at all (unless explicitly instructed otherwise). If no error occurs, the entire set of statements is committed (written) to the database tables. If an error does occur, then a rollback (undo) can occur to restore the database to a known and safe state. So, looking at the same example, this is how the process would work: 1. Check if the customer is already in the database; if not add him or her.

Managing Transaction Processing

2. Commit the customer information. 3. Retrieve the customer’s ID. 4. Add a row to the Orders table. 5. If a failure occurs while adding the row to Orders, roll back. 6. Retrieve the new order ID assigned in the Orders table. 7. Add one row to the OrderItems table for each item ordered. 8. If a failure occurs while adding rows to OrderItems, roll back all the OrderItems rows added and the Orders row. When working with transactions and transaction processing, there are a few keywords that’ll keep reappearing. Here are the terms you need to know: •

Transaction

•

Rollback

•

Commit

•

Savepoint

A block of SQL statements

The process of undoing specified SQL statements Writing unsaved SQL statements to the database tables

A temporary placeholder in a transaction set to which you can issue a rollback (as opposed to rolling back an entire transaction)

Which Statements Can You Roll Back? Transaction processing is used to manage INSERT, UPDATE, and DELETE statements. You cannot roll back SELECT statements. (There would not be much point in doing so anyway.) You cannot roll back CREATE or DROP operations. These statements may be used in a transaction block, but if you perform a rollback they will not be undone.

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Controlling Transactions Now that you know what transactions processing is, let’s look at what is involved in managing transactions.

Implementation Differences The exact syntax used to implement transaction processing differs from one DBMS to another. Refer to your DBMS documentation before proceeding.

The key to managing transactions involves breaking your SQL statements into logical chunks and explicitly stating when data should be rolled back and when it should not. Some DBMSs require that you explicitly mark the start and end of transaction blocks. In SQL Server, for example, you can do the following:

INPUT

BEGIN TRANSACTION ... COMMIT TRANSACTION

In this example, any SQL between the BEGIN TRANSACTION and COMMIT TRANSACTION statements must be executed entirely or not at all.

ANALYSIS

The equivalent code in MySQL is:

INPUT

START TRANSACTION ...

PostgreSQL uses the ANSI SQL syntax:

INPUT

BEGIN; ...

Other DBMSs use variations of the above.

Using ROLLBACK The SQL ROLLBACK command is used to roll back (undo) SQL statements, as seen in this next statement:

Managing Transaction Processing

INPUT

DELETE FROM Orders; ROLLBACK;

In this example, a DELETE operation is performed and then undone using a ROLLBACK statement. Although not the most useful example, it does demonstrate that, within a transaction block, DELETE operations (like INSERT and UPDATE operations) are never final.

ANALYSIS

Using COMMIT Usually SQL statements are executed and written directly to the database tables. This is known as an implicit commit—the commit (write or save) operation happens automatically. Within a transaction block, however, commits might not occur implicitly. This, too, is DBMS specific. Some DBMSs treat a transaction end as an implicit commit; others do not. To force an explicit commit, the COMMIT statement is used. The following is a SQL Server example:

INPUT

BEGIN TRANSACTION DELETE OrderItems WHERE order_num = 12345 DELETE Orders WHERE order_num = 12345 COMMIT TRANSACTION

In this SQL Server example, order number 12345 is deleted entirely from the system. Because this involves updating two database tables, Orders and OrderItems, a transaction block is used to ensure that the order is not partially deleted. The final COMMIT statement writes the change only if no error occurred. If the first DELETE worked, but the second failed, the DELETE would not be committed.

ANALYSIS

To accomplish the same thing in Oracle, you can do the following:

INPUT

DELETE OrderItems WHERE order_num = 12345; DELETE Orders WHERE order_num = 12345; COMMIT;

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Using Savepoints Simple ROLLBACK and COMMIT statements enable you to write or undo an entire transaction. Although this works for simple transactions, more complex transactions might require partial commits or rollbacks. For example, the process of adding an order described previously is a single transaction. If an error occurs, you only want to roll back to the point before the Orders row was added. You do not want to roll back the addition to the Customers table (if there was one). To support the rollback of partial transactions, you must be able to put placeholders at strategic locations in the transaction block. Then, if a rollback is required, you can roll back to one of the placeholders. In SQL, these placeholders are called savepoints. To create one in MySQL and Oracle, the SAVEPOINT statement is used, as follows:

INPUT

SAVEPOINT delete1;

In SQL Server and Sybase, you do the following:

INPUT

SAVE TRANSACTION delete1;

Each savepoint takes a unique name that identifies it so that, when you roll back, the DBMS knows where you are rolling back to. To roll back to this savepoint, do the following in SQL Server:

INPUT

ROLLBACK TRANSACTION delete1;

In MySQL and Oracle you can do the following:

INPUT

ROLLBACK TO delete1;

The following is a complete SQL Server example:

INPUT

BEGIN TRANSACTION INSERT INTO Customers(cust_id, cust_name) VALUES(‘1000000010’, ‘Toys Emporium’); SAVE TRANSACTION StartOrder; INSERT INTO Orders(order_num, order_date, cust_id) VALUES(20100,’2001/12/1’,’1000000010’);

Managing Transaction Processing

IF @@ERROR 0 ROLLBACK TRANSACTION StartOrder; INSERT INTO OrderItems(order_num, order_item, ➥prod_id, quantity, item_price) VALUES(20100, 1, ‘BR01’, 100, 5.49); IF @@ERROR 0 ROLLBACK TRANSACTION StartOrder; INSERT INTO OrderItems(order_num, order_item, ➥prod_id, quantity, item_price) VALUES(20100, 2, ‘BR03’, 100, 10.99); IF @@ERROR 0 ROLLBACK TRANSACTION StartOrder; COMMIT TRANSACTION

Here are a set of four INSERT statements enclosed within a transaction block. A savepoint is defined after the first INSERT so that, if any of the subsequent INSERT operations fail, the transaction is only rolled back that far. In SQL Server, a variable named @@ERROR can be inspected to see if an operation succeeded. (Other DBMSs use different functions or variables to return this information.) If @@ERROR returns a value other than 0, an error occurred, and the transaction rolls back to the savepoint. If the entire transaction is processed, a COMMIT is issued to save the data.

ANALYSIS

The More Savepoints the Better You can have as many savepoints as you’d like within your SQL code, and the more the better. Why? Because the more savepoints you have the more flexibility you have in managing rollbacks exactly as you need them.

Summary In this lesson, you learned that transactions are blocks of SQL statements that must be executed as a batch. You learned how to use the COMMIT and ROLLBACK statements to explicitly manage when data is written and when it is undone. You also learned how to use savepoints to provide a greater level of control over rollback operations.

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LESSON 21

Using Cursors In this lesson, you’ll learn what cursors are and how to use them.

Understanding Cursors SQL retrieval operations work with sets of rows known as result sets. The rows returned are all the rows that match a SQL statement—zero or more of them. Using simple SELECT statements, there is no way to get the first row, the next row, or the previous 10 rows. This is an integral part of how a relational DBMS works. Result Set The results retrieved by a SQL query. Sometimes there is a need to step through rows forward or backward and one or more at a time. This is what cursors are used for. A cursor is a database query stored on the DBMS server—not a SELECT statement, but the result set retrieved by that statement. Once the cursor is stored, applications can scroll or browse up and down through the data as needed.

MySQL Support As this book goes to press, MySQL still does not support cursors (support for views is planned for MySQL 5).

Different DBMSs support different cursor options and features. Some of the more common ones are: • The capability to flag a cursor as read-only so that data can be read but not updated or deleted

• The capability to control the directional operations that can be performed (forward, backward, first, last, absolute position, relative position, and so on) • The capability to flag some columns as editable and others as not editable • Scope specification so as to be able to make the cursor accessible to a specific request that created it (a stored procedure, for example) or to all requests • Instructing the DBMS to make a copy of the retrieved data (as opposed to pointing to the live data in the table) so that data does not change between the time the cursor is opened and the time it is accessed

Making Relational DBMSs Behave Like Nonrelational DBMSs As a point of reference, accessing and browsing rows in this fashion is actually the behavior of ISAM (Indexed Sequential Access Method) databases (such as Btrieve and dBASE). Cursors are an interesting part of the SQL specification in that they can make a relational database behave like an ISAM database.

Cursors are used primarily by interactive applications in which users need to scroll up and down through screens of data, browsing or making changes.

Cursors and Web-Based Applications Cursors are rather useless when it comes to Web-based applications (ASP, ColdFusion, PHP, and JSP, for example). Cursors are designed to persist for the duration of a session between a client application and a server, but this client/server model does not fit in the Web application world because the application server is the database client, not the end user. As such, most Web application developers avoid the use of cursors and recreate the functionality themselves if needed.

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Working with Cursors Using cursors involves several distinct steps: • Before a cursor can be used it must be declared (defined). This process does not actually retrieve any data, it merely defines the SELECT statement to be used and any cursor options. • Once it is declared, the cursor must be opened for use. This process actually retrieves the data using the previously defined SELECT statement. • With the cursor populated with data, individual rows can be fetched (retrieved) as needed. • When it is done, the cursor must be closed and possibly deallocated (depending on the DBMS). Once a cursor is declared, it may be opened and closed as often as needed. Once it is open, fetch operations can be performed as often as needed.

Creating Cursors Cursors are created using the DECLARE statement, which differs from one DBMS to the next. DECLARE names the cursor and takes a SELECT statement, complete with WHERE and other clauses if needed. To demonstrate this, we’ll create a cursor that retrieves all customers without email addresses, as part of an application enabling an operator to provide missing email addresses. Here is the DB2, SQL Server, and Sybase version:

INPUT

DECLARE CustCursor CURSOR FOR SELECT * FROM Customers WHERE cust_email IS NULL

Here is the Oracle and PostgreSQL version:

INPUT

DECLARE CURSOR CustCursor IS SELECT * FROM Customers WHERE cust_email IS NULL

Using Cursors

In both versions, the DECLARE statement is used to define and name the cursor—in this case CustCursor. The SELECT statement defines a cursor containing all customers with no email address (a NULL value).

ANALYSIS

Now that the cursor is defined, it is ready to be opened.

Using Cursors Cursors are opened using the OPEN CURSOR statement, which is so simple a statement that most DBMSs support exactly the same syntax:

INPUT

OPEN CURSOR CustCursor

When the OPEN CURSOR statement is processed, the query is executed, and the retrieved data is stored for subsequent browsing and scrolling.

ANALYSIS

Now the cursor data can be accessed using the FETCH statement. FETCH specifies the rows to be retrieved, where they are to be retrieved from, and where they are to be stored (variable names, for example). The first example uses Oracle syntax to retrieve a single row from the cursor (the first row):

INPUT

DECLARE TYPE CustCursor IS REF CURSOR ➥ RETURN Customers%ROWTYPE; DECLARE CustRecord Customers%ROWTYPE BEGIN OPEN CustCursor; FETCH CustCursor INTO CustRecord; CLOSE CustCursor; END;

In this example, FETCH is used to retrieve the current row (it’ll start at the first row automatically) into a declared variable named CustRecord. Nothing is done with the retrieved data.

ANALYSIS

In the next example (again, using Oracle syntax), the retrieved data is looped through from the first row to the last:

INPUT

DECLARE TYPE CustCursor IS REF CURSOR ➥RETURN Customers%ROWTYPE; DECLARE CustRecord Customers%ROWTYPE

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BEGIN OPEN CustCursor; LOOP FETCH CustCursor INTO CustRecord; EXIT WHEN CustCursor%NOTFOUND; ... END LOOP; CLOSE CustCursor; END;

Like the previous example, this example uses FETCH to retrieve the current row into a declared variable named CustRecord. Unlike the previous example, the FETCH here is within a LOOP so that it is repeated over and over. The code EXIT WHEN CustCursor%NOTFOUND causes processing to be terminated (exiting the loop) when there are no more rows to be fetched. This example also does no actual processing; in real-world code you’d replace the ... placeholder with your own code.

ANALYSIS

Here’s another example, this time using Microsoft SQL Server syntax:

INPUT

DECLARE @cust_id CHAR(10), @cust_name CHAR(50), @cust_address CHAR(50), @cust_city CHAR(50), @cust_state CHAR(5), @cust_zip CHAR(10), @cust_country CHAR(50), @cust_contact CHAR(50), @cust_email CHAR(255), OPEN CustCursor FETCH NEXT FROM CustCursor INTO @cust_id, @cust_name, @cust_address, @cust_city, @cust_state, @cust_zip, @cust_country, @cust_contact, @cust_email WHILE @@FETCH_STATUS = 0 BEGIN ... FETCH NEXT FROM CustCursor INTO @cust_id, @cust_name, @cust_address, @cust_city, @cust_state, @cust_zip, @cust_country, @cust_contact, @cust_email END CLOSE CustCursor

Using Cursors

In this example, variables are declared for each of the retrieved columns, and the FETCH statements retrieve a row and save the values into those variables. A WHILE loop is used to loop through the rows, and the condition WHILE @@FETCH_STATUS = 0 causes processing to be terminated (exiting the loop) when there are no more rows to be fetched. Again, this example does no actual processing; in realworld code you’d replace the ... placeholder with your own code.

ANALYSIS

Closing Cursors As already mentioned and seen in the previous examples, cursors need to be closed after they have been used. In addition, some DBMSs (such as SQL Server) require that the resources used by the cursor be explicitly deallocated. Here’s the DB2, Oracle, and PostgreSQL syntax:

INPUT

CLOSE CustCursor

Here’s the Microsoft SQL Server version:

INPUT

CLOSE CustCursor DEALLOCATE CURSOR CustCursor

The CLOSE statement is used to close cursors; once a cursor is closed, it cannot be reused without being opened again. However, a cursor does not need to be declared again to be used; an OPEN is sufficient.

ANALYSIS

Summary In this lesson, you learned what cursors are and why they are used. Your own DBMS probably offers some form of this function, as well as others not mentioned here. Refer to your DBMS documentation for more details.

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LESSON 22

Understanding Advanced SQL Features In this lesson, you’ll look at several of the advanced data-manipulation features that have evolved with SQL: constraints, indexes, and triggers.

Understanding Constraints SQL has evolved through many versions to become a very complete and powerful language. Many of the more powerful features are sophisticated tools that provide you with data-manipulation techniques such as constraints. Relational tables and referential integrity have both been discussed several times in prior lessons. As I explained in those lessons, relational databases store data broken into multiple tables, each of which stores related data. Keys are used to create references from one table to another (thus the term referential integrity). For relational database designs to work properly, you need a way to ensure that only valid data is inserted into tables. For example, if the Orders table stores order information and OrderItems stores order details, you want to ensure that any order IDs referenced in OrderItems exist in Orders. Similarly, any customers referred to in Orders must be in the Customers table. Although you can perform checks before inserting new rows (do a SELECT on another table to make sure the values are valid and present), it is best to avoid this practice for the following reasons:

• If database integrity rules are enforced at the client level, every client is obliged to enforce those rules, and inevitably some clients won’t. • You must also enforce the rules on UPDATE and DELETE operations. • Performing client-side checks is a time-consuming process. Having the DBMS do the checks for you is far more efficient. Constraints Rules that govern how database data is inserted or manipulated. DBMSs enforce referential integrity by imposing constraints on database tables. Most constraints are defined in table definitions (using the CREATE TABLE or ALTER TABLE as discussed in Lesson 17, “Creating and Manipulating Tables”).

Caution There are several different types of constraints, and each DBMS provides its own level of support for them. Therefore, the examples shown here might not work as you see them. Refer to your DBMS documentation before proceeding.

Primary Keys I discussed primary keys briefly in Lesson 1, “Understanding SQL.” A primary key is a special constraint that is used to ensure that values in a column (or set of columns) are unique and never change, in other words, a column (or columns) in a table whose values uniquely identify each row in the table. This facilitates the direct manipulation of and interaction with individual rows. Without primary keys, it would be very difficult to safely UPDATE or DELETE specific rows without affecting any others.

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Any column in a table can be established as the primary key, as long as it meets the following conditions: • No two rows may have the same primary key value. • Every row must have a primary key value. (Columns must not allow NULL values.) • The column containing primary key values can never be modified or updated. • Primary key values can never be reused. If a row is deleted from the table, its primary key must not be assigned to any new rows. One way to define primary keys is to create them, as follows:

INPUT

ANALYSIS

INPUT ANALYSIS in CREATE

CREATE TABLE Vendors ( vend_id CHAR(10) vend_name CHAR(50) vend_address CHAR(50) vend_city CHAR(50) vend_state CHAR(5) vend_zip CHAR(10) vend_country CHAR(50) );

NOT NULL PRIMARY KEY, NOT NULL, NULL, NULL, NULL, NULL NULL

In the above example, the keyword PRIMARY KEY is added to the table definition so that vend_id becomes the primary key. ALTER TABLE Vendors ADD CONSTRAINT PRIMARY KEY (vend_id);

Here the same column is defined as the primary key, but the CONSTRAINT syntax is used instead. This syntax can be used TABLE and ALTER TABLE statements.

Foreign Keys A foreign key is a column in a table whose values must be listed in a primary key in another table. Foreign keys are an extremely important part of ensuring referential integrity. To understand foreign keys, let’s look at an example.

Understanding Advanced SQL Features

The Orders table contains a single row for each order entered into the system. Customer information is stored in the Customers table. Orders in Orders are tied to specific rows in the Customers table by the customer ID. The customer ID is the primary key in the Customers table; each customer has a unique ID. The order number is the primary key in the Orders table; each order has a unique number. The values in the customer ID column in the Orders table are not necessarily unique. If a customer has multiple orders, there will be multiple rows with the same customer ID (although each will have a different order number). At the same time, the only values that are valid within the customer ID column in Orders are the IDs of customers in the Customers table. That’s what a foreign key does. In our example, a foreign key is defined on the customer ID column in Orders so that the column can accept only values that are in the Customers table’s primary key. Here’s one way to define this foreign key:

INPUT

CREATE TABLE Orders ( order_num INTEGER order_date DATETIME cust_id CHAR(10) ➥Customers(cust_id) );

NOT NULL PRIMARY KEY, NOT NULL, NOT NULL REFERENCES

Here the table definition uses the REFERENCES keyword to state that any values in cust_id must be in cust_id in the Customers table.

ANALYSIS

The same thing could have been accomplished using CONSTRAINT syntax in an ALTER TABLE statement:

INPUT

ALTER TABLE Customers ADD CONSTRAINT FOREIGN KEY (cust_id) REFERENCES Customers (cust_id)

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Foreign Keys Can Help Prevent Accidental Deletion In addition to helping enforce referential integrity, foreign keys serve another invaluable purpose. After a foreign key is defined, your DBMS does not allow the deletion of rows that have related rows in other tables. For example, you are not allowed to delete a customer who has associated orders. The only way to delete that customer is to first delete the related orders (which in turn means deleting the related order items). Because they require such methodical deletion, foreign keys can help prevent the accidental deletion of data. However, some DBMSs support a feature called cascading delete. If enabled, this feature deletes all related data when a row is deleted from a table. For example, if cascading delete is enabled and a customer is deleted from the Customers table, any related order rows are deleted automatically.

Unique Constraints Unique constraints are used to ensure that all data in a column (or set of columns) is unique. They are similar to primary keys, but there are some important distinctions: • A table can contain multiple unique constraints, but only one primary key is allowed per table. • Unique constraint columns can contain NULL values. • Unique constraint columns can be modified or updated. • Unique constraint column values can be reused. • Unlike primary keys, unique constraints cannot be used to define foreign keys. An example of the use of constraints is an employees table. Every employee has a unique Social Security number, but you would not want to

Understanding Advanced SQL Features

use it for the primary key because it is too long (in addition to the fact that you might not want that information easily available). Therefore, every employee also has a unique employee ID (a primary key) in addition to his Social Security number. Because the employee ID is a primary key, you can be sure that it is unique. You also might want the DBMS to ensure that each Social Security number is unique, too (to make sure that a typo does not result in the use of someone else’s number). You can do this by defining a UNIQUE constraint on the Social Security number column. The syntax for unique constraints is similar to that for other constraints. Either the UNIQUE keyword is defined in the table definition or a separate CONSTRAINT is used.

Check Constraints Check constraints are used to ensure that data in a column (or set of columns) meets a set of criteria that you specify. Common uses of this are • Checking minimum or maximum values—for example, preventing an order of 0 (zero) items (even though 0 is a valid number) • Specifying ranges—for example, making sure that a ship date is greater than or equal to today’s date and not greater than a year from now • Allowing only specific values—for example, allowing only M or F in a gender field In other words, datatypes (discussed in Lesson 1) restrict the type of data that can be stored in a column. Check constraints place further restrictions within that datatype. The following example applies a check constraint to the OrderItems table to ensure that all items have a quantity greater than 0:

INPUT

CREATE TABLE OrderItems ( order_num INTEGER order_item INTEGER prod_id CHAR(10)

NOT NULL, NOT NULL, NOT NULL,

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quantity INTEGER ➥(quantity > 0), item_price MONEY );

ANALYSIS

NOT NULL CHECK NOT NULL

With this constraint in place, any row inserted (or updated) will be checked to ensure that quantity is greater than 0.

To check that a column named gender contains only M or F, you can do the following in an ALTER TABLE statement:

INPUT

ADD CONSTRAINT CHECK (gender LIKE ‘[MF]’)

User-Defined Datatypes Some DBMSs enable you to define your own datatypes. These are essentially simple datatypes with check constraints (or other constraints) defined. For example, you can define your own datatype called gender that is a single-character text datatype with a check constraint that restricts its values to M or F (and perhaps NULL for Unknown). You could then use this datatype in table definitions. The advantage of custom datatypes is that the constraints need to be applied only once (in the datatype definition), and they are automatically applied each time the datatype is used. Check your DBMS documentation to determine if user-defined datatypes are supported.

Understanding Indexes Indexes are used to sort data logically to improve the speed of searching and sorting operations. The best way to understand indexes is to envision the index at the back of a book (this book, for example). Suppose you want to find all occurrences of the word datatype in this book. The simple way to do this would be to turn to page 1 and scan every line of every page looking for matches. Although that works, it is

Understanding Advanced SQL Features

obviously not a workable solution. Scanning a few pages of text might be feasible, but scanning an entire book in that manner is not. As the amount of text to be searched increases, so does the time it takes to pinpoint the desired data. That is why books have indexes. An index is an alphabetical list of words with references to their locations in the book. To search for datatype, you find that word in the index to determine what pages it appears on. Then, you turn to those specific pages to find your matches. What makes an index work? Simply, it is the fact that it is sorted correctly. The difficulty in finding words in a book is not the amount of content that must be searched; rather, it is the fact that the content is not sorted by word. If the content is sorted like a dictionary, an index is not needed (which is why dictionaries don’t have indexes). Database indexes work in much the same way. Primary key data is always sorted; that’s just something the DBMS does for you. Retrieving specific rows by primary key, therefore, is always a fast and efficient operation. Searching for values in other columns is usually not as efficient, however. For example, what if you want to retrieve all customers who live in a specific state? Because the table is not sorted by state, the DBMS must read every row in the table (starting at the very first row) looking for matches, just as you would have to do if you were trying to find words in a book without using an index. The solution is to use an index. You may define an index on one or more columns so that the DBMS keeps a sorted list of the contents for its own use. After an index is defined, the DBMS uses it in much the same way as you would use a book index. It searches the sorted index to find the location of any matches and then retrieves those specific rows. But before you rush off to create dozens of indexes, bear in mind the following: • Indexes improve the performance of retrieval operations, but they degrade the performance of data insertion, modification, and deletion. When these operations are executed, the DBMS has to update the index dynamically. • Index data can take up lots of storage space.

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• Not all data is suitable for indexing. Data that is not sufficiently unique (State, for example) will not benefit as much from indexing as data that has more possible values (First Name or Last Name, for example). • Indexes are used for data filtering and for data sorting. If you frequently sort data in a specific order, that data might be a candidate for indexing. • Multiple columns can be defined in an index (for example, State plus City). Such an index will be of use only when data is sorted in State plus City order. (If you want to sort by City, this index would not be of any use.) There is no hard-and-fast rule as to what should be indexed and when. Most DBMSs provide utilities you can use to determine the effectiveness of indexes, and you should use these regularly. Indexes are created with the CREATE INDEX statement (which varies dramatically from one DBMS to another). The following statement creates a simple index on the Products table’s product name column:

INPUT

CREATE INDEX prod_name_ind ON PRODUCTS (prod_name);

Every index must be uniquely named. Here the name prod_name_ind is defined after the keywords CREATE INDEX. ON is used to specify the table being indexed, and the columns to include in the index (just one in this example) are specified in parentheses after the table name.

ANALYSIS

Revisiting Indexes Index effectiveness changes as table data is added or changed. Many database administrators find that what once was an ideal set of indexes might not be so ideal after several months of data manipulation. It is always a good idea to revisit indexes on a regular basis to fine-tune them as needed.

Understanding Advanced SQL Features

Understanding Triggers Triggers are special stored procedures that are executed automatically when specific database activity occurs. Triggers might be associated with INSERT, UPDATE, and DELETE operations (or any combination thereof) on specific tables.

MySQL Support As this book goes to press, MySQL still does not support triggers (support for triggers is planned for MySQL 5.1).

Unlike stored procedures (which are simply stored SQL statements), triggers are tied to individual tables. A trigger associated with INSERT operations on the Orders table will be executed only when a row is inserted into the Orders table. Similarly, a trigger on INSERT and UPDATE operations on the Customers table will be executed only when those specific operations occur on that table. Within triggers, your code has access to the following: • All new data in INSERT operations • All new data and old data in UPDATE operations • Deleted data in DELETE operations Depending on the DBMS being used, triggers can be executed before or after a specified operation is performed. The following are some common uses for triggers: • Ensuring data consistency—for example, converting all state names to uppercase during an INSERT or UPDATE operation • Performing actions on other tables based on changes to a table— for example, writing an audit trail record to a log table each time a row is updated or deleted

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• Performing additional validation and rolling back data if needed—for example, making sure a customer’s available credit has not been exceeded and blocking the insertion if it has • Calculating computed column values or updating timestamps As you probably expect by now, trigger creation syntax varies dramatically from one DBMS to another. Check your documentation for more details. The following example creates a trigger that converts the cust_state column in the Customers table to uppercase on all INSERT and UPDATE operations. This is the SQL Server version:

INPUT

CREATE TRIGGER customer_state ON Customers FOR INSERT, UPDATE AS UPDATE Customers SET cust_state = Upper(cust_state) WHERE Customers.cust_id = inserted.cust_id;

This is the Oracle and PostgreSQL version:

INPUT

CREATE TRIGGER customer_state AFTER INSERT OR UPDATE FOR EACH ROW BEGIN UPDATE Customers SET cust_state = Upper(cust_state) WHERE Customers.cust_id = :OLD.cust_id END;

Constraints Are Faster Than Triggers As a rule, constraints are processed more quickly than triggers, so whenever possible, use constraints instead.

Understanding Advanced SQL Features

Database Security There is nothing more valuable to an organization than its data, and data should always be protected from would-be thieves or casual browsers. Of course, at the same time data must be accessible to users who need access to it, and so most DBMSs provide administrators with mechanisms by which to grant or restrict access to data. The foundation of any security system is user authorization and authentication. This is the process by which a user is validated to ensure he is who he says he is and that he is allowed to perform the operation he is trying to perform. Some DBMSs integrate with operating system security for this, others maintain their own user and password lists, and still others integrate with external directory services servers. Some operations that are often secured • Access to database administration features (creating tables, altering or dropping existing tables, and so on) • Access to specific databases or tables • The type of access (read-only, access to specific columns, and so on) • Access to tables via views or stored procedures only • Creation of multiple levels of security, thus allowing varying degrees of access and control based on login • Restricting the ability to manage user accounts Security is managed via the SQL GRANT and REVOKE statements, although most DBMSs provide interactive administration utilities that use the GRANT and REVOKE statements internally.

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Summary In this lesson, you learned how to use some advanced SQL features. Constraints are an important part of enforcing referential integrity; indexes can improve data retrieval performance; triggers can be used to perform pre- or post-execution processing; and security options can be used to manage data access. Your own DBMS probably offers some form of these features. Refer to your DBMS documentation for more details.

APPENDIX A

Sample Table Scripts Writing SQL statements requires a good understanding of the underlying database design. Without knowing what information is stored in what table, how tables are related to each other, and the actual breakup of data within a row, it is impossible to write effective SQL. You are strongly advised to actually try every example in every lesson in this book. All the lessons use a common set of data files. To assist you in better understanding the examples, and to enable you to follow along with the lessons, this appendix describes the tables used, their relationships, and how to build (or obtain) them.

Understanding the Sample Tables The tables used throughout this book are part of an order entry system used by an imaginary distributor of toys. The tables are used to perform several tasks: • Manage vendors • Manage product catalogs • Manage customer lists • Enter customer orders Making this all work requires five tables (that are closely interconnected as part of a relational database design). A description of each of the tables appears in the following sections.

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Simplified Examples The tables used here are by no means complete. A real-world order entry system would have to keep track of lots of other data that has not been included here (for example, payment and accounting information, shipment tracking, and more). However, these tables do demonstrate the kinds of data organization and relationships that you will encounter in most real installations. You can apply these techniques and technologies to your own databases.

Table Descriptions What follows is a description of each of the five tables, along with the name of the columns within each table and their descriptions.

The Vendors Table The Vendors table stores the vendors whose products are sold. Every vendor has a record in this table, and that vendor ID (the vend_id) column is used to match products with vendors.

Table A.1

Vendors Table Columns

Column

Description

vend_id

Unique vendor ID

vend_name

Vendor name

vend_address

Vendor address

vend_city

Vendor city

vend_state

Vendor state

vend_zip

Vendor zip code

vend_country

Vendor country

• All tables should have primary keys defined. This table should use vend_id as its primary key.

Sample Table Scripts

The Products Table The Products table contains the product catalog, one product per row. Each product has a unique ID (the prod_id column) and is related to its vendor by vend_id (the vendor’s unique ID).

Table A.2

Products Table Columns

Column

Description

prod_id

Unique product ID

vend_id

Product vendor ID (relates to vend_id in Vendors table)

prod_name

Product name

prod_price

Product price

prod_desc

Product description

• All tables should have primary keys defined. This table should use prod_id as its primary key. • To enforce referential integrity, a foreign key should be defined on vend_id relating it to vend_id in VENDORS.

The Customers Table The Customers table stores all customer information. Each customer has a unique ID (the cust_id column).

Table A.3

Customers Table Columns

Column

Description

cust_id

Unique customer ID

cust_name

Customer name

cust_address

Customer address

cust_city

Customer city continues

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Table A.3

Continued

Column

Description

cust_state

Customer state

cust_zip

Customer zip code

cust_country

Customer country

cust_contact

Customer contact name

cust_email

Customer contact email address

• All tables should have primary keys defined. This table should use cust_id as its primary key.

The Orders Table The Orders table stores customer orders (but not order details). Each order is uniquely numbered (the order_num column). Orders are associated with the appropriate customers by the cust_id column (which relates to the customer’s unique ID in the Customers table).

Table A.4

Orders Table Columns

Column

Description

order_num

Unique order number

order_date

Order date

cust_id

Order customer ID (relates to cust_id in Customers table)

• All tables should have primary keys defined. This table should use order_num as its primary key. • To enforce referential integrity, a foreign key should be defined on cust_id relating it to cust_id in CUSTOMERS.

Sample Table Scripts

The OrderItems Table The OrderItems table stores the actual items in each order, one row per item per order. For every row in Orders there are one or more rows in OrderItems. Each order item is uniquely identified by the order number plus the order item (first item in order, second item in order, and so on). Order items are associated with their appropriate order by the order_num column (which relates to the order’s unique ID in Orders). In addition, each order item contains the product ID of the item orders (which relates the item back to the Products table).

Table A.5

OrderItems Table Columns

Column

Description

order_num

Order number (relates to order_num in Orders table)

order_item

Order item number (sequential within an order)

prod_id

Product ID (relates to prod_id in Products table)

quantity

Item quantity

item_price

Item price

• All tables should have primary keys defined. This table should use order_num and order_item as its primary keys. • To enforce referential integrity, foreign keys should be defined on order_num relating it to order_num in Orders and prod_id relating it to prod_id in Products.

Obtaining the Sample Tables In order to follow along with the examples, you need a set of populated tables. Everything you need to get up and running can be found on this book’s Web page at http://www.forta.com/books/0672325675/.

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Download a Ready-To-Use Microsoft Access MDB File You may download a fully populated Microsoft Access MDB file from the above URL. If you use this file you will not need to run any of the SQL creation and population scripts. The Access MDB file may be used with any ODBC client utilities, as well as via scripting languages like ASP and ColdFusion.

Download DBMS SQL Scripts Most DBMSs store data in formats that do not lend themselves to complete file distribution (as Access does). For these DBMSs you may download SQL scripts from http://www.forta.com/books/0672325675/. There are two files for each DBMS: contains the SQL statements to create the five database tables (including defining all primary keys and foreign key constraints).

•

create.txt

•

populate.txt contains the SQL INSERT statements used to populate these tables.

The SQL statements in these files are very DBMS specific, so be sure to execute the one for your own DBMS. These scripts are provided as a convenience to readers, and no liability is assumed for problems that might arise from their use. At the time that this book went to press, scripts were available for: • IBM DB2 • Microsoft SQL Server • MySQL • Oracle • PostgreSQL • Sybase Adaptive Server Other DBMSs may be added as needed or requested.

Sample Table Scripts

Appendix B, “Working in Popular Applications,” provides instructions on running the scripts in several popular environment.

Create, Then Populate You must run the table creation scripts before the table population scripts. Be sure to check for any error messages returned by these scripts. If the creation scripts fail you will need to remedy whatever problem might exist before continuing with table population.

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APPENDIX B

Working in Popular Applications As explained in Lesson 1, “Understanding SQL,” SQL is not an application, it is a language. To follow along with the examples in this book, you need an application that supports the execution of SQL statements. This appendix describes the steps for executing SQL statements in some of the more commonly used applications. You can use any application listed below, and many others, to test and experiment with SQL code. So which should you use? • Many DBMSs come with their own client utilities, so those are a good place to start. However, these tend to not have the most intuitive user interfaces. • Windows users likely have a utility named Microsoft Query on their computers. This is a simple utility that is very effective for testing simple statements. • A wonderful Windows only option is George Poulose’s Query Tool. There is a link to this on the book Web page at http://www.forta.com/books/0672325675/. • Aqua Data Studio is an incredibly useful free Java based utility that will run on Windows, Linux, Unix, Mac OSX, and other computers. There is a link to this utility on the book Web page at http://www.forta.com/books/0672325675/. Any of these are good options, and there are others too. For additional recommendations visit the book Web page.

Using Aqua Data Studio Aqua Data Studio is a free Java based SQL client. It runs on all major platforms, and supports all major DBMSs (as well as ODBC). To execute a SQL statement in Aqua Data Studio, do the following: 1. Launch Aqua Data Studio. 2. DBMSs must be registered before they can be used. Select Register Server from the Server menu. 3. Select the DBMS you are using from the displayed list (select Generic ODBC to use Microsoft Access or any ODBC data base, this requires that an ODBC data source be defined as explained at the end of this appendix). Based on the DBMS selected, you will be prompted for path or login information. Fill in the form and click OK. Once registered, the server will appear in the list on the left. 4. Select a server from the list of registered servers. 5. Launch the Query Analyzer by selecting Query Analyzer from the Server menu, or by pressing Ctrl-Q. 6. Type your SQL in the query window (the top window). 7. To execute your SQL, select Execute from the Query menu, or press Ctrl-E, or click the Execute button (the one with the green arrow). 8. Results will be displayed in the lower window.

Using DB2 IBM’s DB2 is a powerful high-end, multiplatform DBMS. It comes with a whole suite of client tools that may be used to execute SQL statements. The instructions that follow use the Java based Command Center utility because it is one of the simplest and most versatile of the bundled applications: 1. Launch the Command Center. 2. Select the Script tab.

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Appendix B

3. Enter the SQL statement in the Script box. 4. Select Execute from the Script menu, or click the Execute button, to execute the script. 5. Raw data results will be displayed in the lower window. Switch to the Results tab to display results in a grid format. 6. Command Center features an interactive SQL statement builder called SQL Assist. This can be executed from the Interactive tab.

Using Macromedia ColdFusion Macromedia ColdFusion is a Web-application development platform. ColdFusion uses a tag-based language to create scripts. To test your SQL, create a simple page that you can execute by calling it from your Web browser. Perform the following steps: 1. Before using any databases from within ColdFusion code, a Data Source must be defined. The ColdFusion Administrator program provides a Web-based interface to define Data Sources (refer to the ColdFusion documentation for help if needed). 2. Create a new ColdFusion page (with a CFM extension). 3. Use the CFML and tags to create a query block. Name it using the NAME attribute and define the Data Source in the DATASOURCE attribute. 4. Type your SQL statement between the and tags. 5. Use or a loop to display the query results. 6. Save the page in any executable directory beneath the Web server root. 7. Execute the page by calling it from a Web browser.

Working in Popular Applications

Using Microsoft Access Microsoft Access is usually used interactively to create and manage databases and to interact and manipulate data, and Access features a Query Designer that can be used to build a SQL statement interactively. A frequently overlooked feature of this Query Designer is that it also lets you specify SQL for direct execution. This enables you to use Access to send SQL statements to any ODBC Data Source, although it is best suited for executing SQL against an open database. To use this feature, do the following: 1. Launch Microsoft Access. You will be prompted to open (or create) a database. Open the database that you want to use. 2. Select Queries in the Database window. Then click on the New button and select Design View. 3. You’ll be prompted with a Show Table dialog. Close that window without selecting any tables. 4. From the View menu, select SQL View to display the Query window. 5. Type your SQL statement in the Query window. 6. To execute the SQL statement click on the Run button (the one with the red exclamation mark). This will switch the view to Datasheet View (which displays the results in a grid). 7. Toggle between SQL View and Datasheet View as needed (you’ll need to go back to SQL View to change your SQL). You can also use Design View to interactively build SQL statements. Microsoft Access also supports a Pass-Through mode that enables you to use Access to send SQL statements to any ODBC Data Source. This feature should be used to interact with external databases, and never with Access databases directly. To use this feature, do the following: 1. Microsoft Access uses ODBC to interact with databases, so an ODBC Data Source must be present before proceeding (see the earlier instructions).

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Appendix B

2. Launch Microsoft Access. You will be prompted to open (or create) a database. Open any database. 3. Select Queries in the Database window. Then click on the New button and select Design View. 4. You’ll be prompted with a Show Table dialog. Close that window without selecting any tables. 5. From the Query menu, select SQL Specific and then select PassThrough (older versions of Access called this option SQL PassThrough). 6. From the View menu, select Properties to display the Query Properties dialog. 7. Click in the ODBC Connect Str field and then click the … button to display the Select Data Source dialog, which you can use to select the ODBC Data Source. 8. Select your Data Source and click OK to return to the Query Properties dialog. 9. Click on the Returns Records field. If you are executing a SELECT statement (or any statement that returns results), set Returns Records to Yes. If you are executing a SQL statement that does not return data (for example, INSERT, UPDATE, or DELETE) set Return Records to No. 10. Type your SQL statement in the SQL Pass-Through Query window. 11. To execute the SQL statement click on the Run button (the one with the red exclamation mark).

Using Access Pass-Through Mode Access passthrough mode works best when connecting to DBMSs other than Access. When connecting to an Access MDB file you are best off using any of the other client options discussed here.

Working in Popular Applications

Using Microsoft ASP Microsoft ASP is a scripting platform for creating Web-based applications. To test your SQL statements within an ASP page, you must create a page that you can execute by calling it from your Web browser. Here are the steps needed to execute a SQL statement within an ASP page: 1. ASP uses ODBC to interact with databases, so an ODBC Data Source must be present before proceeding (refer to the end of this appendix). 2. Create a new ASP page (with an ASP extension) using any text editor. 3. Use Server.CreateObject to create an instance of the ADODB.Connection object. 4. Use the Open method to open the desired ODBC Data Source. 5. Pass your SQL statement to a call to the Execute method. The Execute method returns a result set. Use a Set command to save the result returned into a result set. 6. To display the results, you must loop through the retrieved data using a loop. 7. Save the page in any executable directory beneath the Web server root. 8. Execute the page by calling it from a Web browser.

Using Microsoft ASP.NET Microsoft ASP.NET is a scripting platform for creating Web-based applications using the .NET framework. To test SQL statements within an ASP.NET page, you must create a page that you can execute by calling it from your browser. There are multiple ways to accomplish this, but here is one option: 1. Create a new file with a .aspx extensions. 2. Create a database connection using SqlConnection() or OleDbConnection().

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Appendix B

3. Use either SqlCommand() or OleDbCommand() to pass the statement to the DBMS. 4. Create a DataReader using ExecuteReader. 5. Loop through the returned reader to obtain the returned values. 6. Save the page in any executable directory beneath the Web server root. 7. Execute the page by calling it from a Web browser.

Using Microsoft Query Microsoft Query is a standalone SQL query tool and is an ideal utility for testing SQL statements against ODBC Data Sources. Microsoft Query is optionally installed with other Microsoft products, as well as with other third-party products.

Obtaining MS-Query MS-Query is often installed with other Microsoft products (for example, Office) although it may only be installed if a complete installation was performed. If it is not present under the Start button, use Start Find to locate it on your system. (It is often present without your knowing it.) The files to look for are MSQRY32.EXE or MSQUERY.EXE.

To use Microsoft Query, do the following: 1. Microsoft Query uses ODBC to interact with databases, so an ODBC Data Source must be present before you can proceed (see the instructions at the end of this appendix). 2. Before you can use Microsoft Query, it must be installed on your computer. Browse your program groups beneath the Start button to locate it.

Working in Popular Applications

3. From the File menu, select Execute SQL to display the Execute SQL window. 4. Click the Data Sources button to select the desired ODBC Data Source. If the Data Source you need is not listed, click Other to locate it. After you have selected the correct Data Source, click the Use button. 5. Type your SQL statement in the SQL Statement box. 6. Click Execute to execute the SQL statement and to display any returned data.

Using Microsoft SQL Server Microsoft SQL Server features a Windows-based query analysis tool called SQL Query Analyzer. Although this tool is primarily designed to analyze SQL statement execution and optimization, it does present an ideal environment for testing and experimenting with SQL statements. Here’s how to use the SQL Query Analyzer: 1. Launch the SQL Query Analyzer application (from the Microsoft SQL Server program group). 2. You’ll be prompted for server and login information. Log in to your SQL Server (starting the server if appropriate). 3. When the query screen is displayed, select the database from the drop-down DB list box on the toolbar. 4. Type your SQL in the large text window, and then click the Execute Query button (the one with the green arrow) to execute it. (You can also click F5 or select Execute from the Query menu.) 5. The results will be displayed in a separate pane beneath the SQL window. 6. Click the tabs at the bottom of the query screen to toggle between seeing data and seeing returned messages and information.

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Appendix B

Using MySQL MySQL comes with a command line utility named mysql. This is a text only tool that can be used to execute any SQL statements. To use mysql, do the following: 1. Type mysql to launch the utility. Depending on how security is defined, you may need to use the –u and –p parameters to specify login information. 2. At the mysql> prompt type USE database (specifying the name of the database to be used) to open your database. 3. Type your SQL at the mysql> prompt, making sure to terminate every statement with a semicolon (;). Results will be displayed on the screen. 4. Type \h for a list of commands that you may use, \s for status information (including MySQL version information). 5. Type \q to quit the mysql utility.

Using Oracle Oracle comes with a Java based management tool called Enterprise Manager. This is actually a suite of tools, one of which is named SQL*Plus Worksheet. Here’s how to use this tool: 1. Launch SQL*Plus Worksheet (either directly, or from within the Oracle Enterprise Manager). 2. You’ll be prompted for login information. Provide a user name and password and connect to the database server. 3. The SQL Worksheet screen is divided into two panes. Type your SQL in the upper pane. 4. To execute the SQL statement, click the Execute button (the one with the picture of the lightning bolt). Results will be displayed in the lower pane.

Working in Popular Applications

Using PHP PHP is a popular Web scripting language. PHP provides functions and libraries used to connect to a variety of databases, and so the code used to execute a SQL statement can vary based on the DBMS used (and how it is being accessed). As such, it is impossible to provide steps that can be used in each and every situation. Refer to PHP documentation for instructions on how to connect to your specific DBMS.

Using PostgreSQL PostgreSQL comes with a command line utility named psql. This is a text only tool that can be used to execute any SQL statements. To use psql, do the following: 1. Type psql to launch the utility. To load a specific database specify it on the command line as psql database (PostgreSQL does not support the USE command). 2. Type your SQL at the => prompt, making sure to terminate every statement with a semicolon (;). Results will be displayed on the screen. 3. Type \? for a list of commands that you may use. 4. Type \h for SQL help, \h statement for help on specific SQL statement (for example, \h SELECT). 5. Type \q to quit the psql utility.

Using Query Tool Query Tool is a standalone SQL query tool created by George Poulose, and is an ideal utility for testing SQL statements against ODBC Data Sources. (There’s an ADO version too).

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Appendix B

Obtaining Query Tool Query Tool can be downloaded from the Web. To obtain a copy follow the link at the book’s Web site: http://www.forta.com/books/ 0672321289/.

To use Query Tool, do the following: 1. Query Tool uses ODBC to interact with databases, so an ODBC Data Source must be present before you can proceed (see the earlier instructions). 2. Before you can use Query Tool, it must be installed on your computer. Browse your program groups beneath the Start button to locate it. 3. A popup dialog will prompt you for the ODBC Data Source to be used. If the Data Source you need is not listed, click New to create it. After you have selected the correct Data Source, click the OK button. 4. Type your SQL statement in the upper right window. 5. Click the Execute button (the one with the blue arrow) to execute the SQL statement and to display any returned data in the lower pane. (You can also click F5 or select Execute from the Query menu.)

Using Sybase Sybase Adaptive Server comes with a Java based utility named SQL Advantage. This utility is very similar to Microsoft SQL Server’s Query Analyzer (the products share a common origin). To use SQL Advantage, do the following: 1. Execute the SQL Advantage application. 2. You will be prompted for login information, provide your login name and password.

Working in Popular Applications

3. When the query screen is displayed, select the database from the drop-down list box on the toolbar. 4. Type your SQL in the window displayed. 5. To execute your query click the Execute button, select Execute Query from the Query menu, or press Ctrl-E. 6. The results (if there are any) will be displayed in a new window.

Configuring ODBC Data Sources Several of the applications described above use ODBC for database integration, and so we’ll start with a brief overview of ODBC and instructions for configuring ODBC Data Sources. ODBC is a standard that is used to enable clients’ applications to interact with different backend databases or underlying database engines. Using ODBC, it is possible to write code in one client and have those tools interact with almost any database or DBMS. ODBC itself is not a database. Rather, ODBC is a wrapper around databases that makes all databases behave in a consistent and clearly defined fashion. It accomplishes this by using software drivers that have two primary functions. First, they encapsulate any native database features or peculiarities and hide these from the client. Second, they provide a common language for interacting with these databases (performing translations when needed). The language used by ODBC is SQL. ODBC client applications do not interact with databases directly. Instead, they interact with ODBC Data Sources. A Data Source is a logical database that includes the driver (each database type has its own driver) and information on how to connect to the database (file paths, server names, and so forth). After ODBC Data Sources are defined, any ODBC-compliant application can use them. ODBC Data Sources are not application specific; they are system specific.

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Appendix B

ODBC Differences There are many different versions of the ODBC applet, making it impossible to provide exact instructions that would apply to all versions. Pay close attention to the prompts when setting up your own Data Sources.

ODBC Data Sources are defined using the Windows Control Panel’s ODBC applet. To set up an ODBC Data Source, do the following: 1. Open the Windows Control Panel’s ODBC applet. 2. Most ODBC Data Sources should be set up to be system-wide Data Sources (as opposed to user-specific Data Sources), so select System DSN, if that option is available to you. 3. Click the Add button to add a new Data Source. 4. Select the driver to use. There is usually a default set of drivers that provides support for major Microsoft products. Other drivers might be installed on your system. You must select a driver that matches the type of database to which you’ll be connecting. 5. Depending on the type of database or DBMS, you are prompted for server name or file path information and possibly login information. Provide this information as requested and then follow the rest of the prompts to create the Data Source.

APPENDIX C

SQL Statement Syntax To help you find the syntax you need when you need it, this appendix lists the syntax for the most frequently used SQL operations. Each statement starts with a brief description and then displays the appropriate syntax. For added convenience, you’ll also find cross references to the lessons where specific statements are taught. When reading statement syntax, remember the following: • The | symbol is used to indicate one of several options, so NULL|NOT NULL means specify either NULL or NOT NULL. • Keywords or clauses contained within square parentheses [like this] are optional. • The syntax listed below will work with almost all DBMSs. You are advised to consult your own DBMS documentation for details of implementing specific syntactical changes.

ALTER TABLE ALTER TABLE is used to update the schema of an existing table. To create a new table, use CREATE TABLE. See Lesson 17, “Creating and Manipulating Tables,” for more information.

INPUT

ALTER TABLE tablename ( ADD|DROP column datatype ➥NULL] [CONSTRAINTS],

[NULL|NOT

208

Appendix C

ADD|DROP column datatype ➥NULL] [CONSTRAINTS], ... );

[NULL|NOT

COMMIT is used to write a transaction to the database. See Lesson 20, “Managing Transaction Processing,” for more information.

COMMIT

INPUT

COMMIT [TRANSACTION];

CREATE INDEX is used to create an index on one or more columns. See Lesson 22, “Understanding Advanced SQL Features,” for more information. CREATE INDEX

INPUT

CREATE INDEX indexname ON tablename (column, ...);

CREATE PROCEDURE is used to create a stored procedure. See Lesson 19, “Working with Stored Procedures,” for more information. Oracle uses a different syntax as described in that lesson.

CREATE PROCEDURE

INPUT

CREATE PROCEDURE procedurename [parameters] ➥[options] AS SQL statement;

CREATE TABLE CREATE TABLE is used to create new database tables. To update the schema of an existing table, use ALTER TABLE. See Lesson 17 for more information.

SQL Statement Syntax

INPUT

CREATE TABLE tablename ( column datatype ➥[CONSTRAINTS], column datatype ➥[CONSTRAINTS], ... );

[NULL|NOT NULL] [NULL|NOT NULL]

CREATE VIEW is used to create a new view of one or more tables. See Lesson 18, “Using Views,” for more information.

CREATE VIEW

INPUT

CREATE VIEW viewname AS SELECT columns, ... FROM tables, ... [WHERE ...] [GROUP BY ...] [HAVING ...];

DELETE DELETE deletes one or more rows from a table. See Lesson 16, “Updating and Deleting Data,” for more information.

INPUT

DELETE FROM tablename [WHERE ...];

DROP DROP permanently removes database objects (tables, views, indexes, and so forth). See Lessons 17 and 18 for more information.

INPUT

DROP INDEX|PROCEDURE|TABLE|VIEW ➥indexname|procedurename|tablename|viewname;

INSERT INSERT adds a single row to a table. See Lesson 15, “Inserting Data,” for more information.

209

210

Appendix C

INPUT

INSERT INTO tablename [(columns, ...)] VALUES(values, ...);

INSERT SELECT inserts the results of a SELECT into a table. See Lesson 15 for more information.

INSERT SELECT

INPUT

INSERT INTO tablename [(columns, ...)] SELECT columns, ... FROM tablename, ... [WHERE ...];

ROLLBACK ROLLBACK is used to undo a transaction block. See Lesson 20 for more information.

INPUT

ROLLBACK [ TO savepointname];

INPUT

ROLLBACK TRANSACTION;

or

SELECT is used to retrieve data from one or more tables (or views). See Lesson 2, “Retrieving Data”; Lesson 3, “Sorting Retrieved Data”; and Lesson 4, “Filtering Data,” for more basic information. (Lessons 2–14 all cover aspects of SELECT.)

SELECT

INPUT

SELECT columnname, ... FROM tablename, ... [WHERE ...] [UNION ...] [GROUP BY ...] [HAVING ...] [ORDER BY ...];

SQL Statement Syntax

UPDATE updates one or more rows in a table. See Lesson 16 for more information.

UPDATE

INPUT

UPDATE tablename SET columname = value, ... [WHERE ...];

211

APPENDIX D

Using SQL Datatypes As explained in Lesson 1, “Understanding SQL,” datatypes are basically rules that define what data may be stored in a column and how that data is actually stored. Datatypes are used for several reasons: • Datatypes enable you to restrict the type of data that can be stored in a column. For example, a numeric datatype column will only accept numeric values. • Datatypes allow for more efficient storage, internally. Numbers and date time values can be stored in a more condensed format than text strings. • Datatypes allow for alternate sorting orders. If everything is treated as strings, 1 comes before 10, which comes before 2. (Strings are sorted in dictionary sequence, one character at a time starting from the left.) As numeric datatypes, the numbers would be sorted correctly. When designing tables, pay careful attention to the datatypes being used. Using the wrong datatype can seriously impact your application. Changing the datatypes of existing populated columns is not a trivial task. (In addition, doing so can result in data loss.) Although this lesson is by no means a complete tutorial on datatypes and how they are to be used, it explains the major datatype types, what they are used for, and compatibility issues that you should be aware of.

No Two DBMSs Are Exactly Alike It’s been said before, but it needs to be said again. Unfortunately, datatypes can vary dramatically from one DBMS to the next. Even the same datatype name can mean different things to different DBMSs. Be sure you consult your DBMS documentation for details on exactly what it supports and how.

String Datatypes The most commonly used datatypes are string datatypes. These store strings: for example, names, addresses, phone numbers, and zip codes. There are basically two types of string datatype that you can use—fixedlength strings and variable-length strings (see Table D.1). Fixed length strings are datatypes that are defined to accept a fixed number of characters, and that number is specified when the table is created. For example, you might allow 30 characters in a first-name column or 11 characters in a social-security-number column (the exact number needed allowing for the two dashes). Fixed-length columns do not allow more than the specified number of characters. They also allocate storage space for as many characters as specified. So, if the string Ben is stored in a 30character first-name field, a full 30 characters are stored (and the text may be padded with spaces or nulls as needed). Variable-length strings store text of any length (the maximum varies by datatype and DBMS). Some variable-length datatypes have a fixed-length minimum. Others are entirely variable. Either way, only the data specified is saved (and no extra data is stored). If variable-length datatypes are so flexible, why would you ever want to used fixed-length datatypes? The answer is performance. DBMSs can sort and manipulate fixed-length columns far more quickly than they can sort variable-length columns. In addition, many DBMSs will not allow you to index variable-length columns (or the variable portion of a column). This also dramatically impacts performance. (See Lesson 22, “Understanding Advanced SQL Features,” for more information on indexes.)

214

Appendix D

Table D.1

String Datatypes

Datatype

Description

CHAR

Fixed length string from 1 to 255 chars long. Its size must be specified at create time.

NCHAR

Special form of CHAR designed to support multibyte or Unicode characters. (The exact specifications vary dramatically from one implementation to the next.)

NVARCHAR

Special form of TEXT designed to support multibyte or Unicode characters. (Exact specifications vary dramatically from one implementation to the next.)

TEXT (also called LONG Variable-length text. or MEMO or VARCHAR)

Using Quotes Regardless of the form of string datatype being used, string values must always be surrounded by single quotes.

When Numeric Values Are Not Numeric Values You might think that phone numbers and zip codes should be stored in numeric fields (after all, they only store numeric data), but doing so would not be advisable. If you store the zip code 01234 in a numeric field, the number 1234 would be saved. You’d actually lose a digit. The basic rule to follow is: If the number is a number used in calculations (sums, averages, and so on), it belongs in a numeric datatype column. If it is used as a literal string (that happens to contain only digits), it belongs in a string datatype column.

Using SQL Datatypes

Numeric Datatypes Numeric datatypes store numbers. Most DBMSs support multiple numeric datatypes, each with a different range of numbers that can be stored in it. Obviously, the larger the supported range, the more storage space needed. In addition, some numeric datatypes support the use of decimal points (and fractional numbers) whereas others support only whole numbers. Table D.2 lists common uses for various datatypes. Not all DBMSs follow the exact naming conventions and descriptions listed here.

Table D.2

Numeric Datatypes

Datatype

Description

BIT

Single bit value, either 0 or 1, used primarily for on/off flags

DECIMAL (also called NUMERIC)

Fixed or floating point values with varying levels of precision

FLOAT (also called NUMBER)

Floating point values

INT (also called INTEGER)

4-byte integer value that supports numbers from –2147483648 to 2147483647

REAL

4-byte floating point values

SMALLINT

2-byte integer value that supports numbers from –32768 to 32767

TINYINT

1-byte integer value that supports numbers from 0 to 255

Not Using Quotes Unlike strings, numeric values should never be enclosed within quotes.

215

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Appendix D

Currency Datatypes Most DBMSs support a special numeric datatype for storing monetary values. Usually called MONEY or CURRENCY, these datatypes are essentially DECIMAL datatypes with specific ranges that make them well-suited for storing currency values.

Date and Time Datatypes All DBMSs support datatypes designed for the storage of date and time values (see Table D.3). Like numeric values, most DBMSs support multiple datatypes, each with different ranges and levels of precision.

Table D.3

Date and Time Datatypes

Datatype

Description

DATE

Date value

DATETIME (also Date time values known as TIMESTAMP) SMALLDATETIME

Date time values with accuracy to the minute (no seconds or milliseconds)

TIME

Time value

Specifying Dates There is no standard way to define a date that will be understood by every DBMS. Most implementations understand formats like 2004-12-30 or Dec 30th, 2004, but even those can be problematic to some DBMSs. Make sure to consult your DBMS documentation for a list of the date formats that it will recognize.

Using SQL Datatypes

ODBC Dates Because every DBMS has its own format for specifying dates, ODBC created a format of its own that will work with every database when ODBC is being used. The ODBC format looks like {d ‘2004-1230’} for dates, {t ‘21:46:29’} for times, and {ts ‘2004-12-30 21:46:29’} for date time values. If you are using SQL via ODBC, be sure your dates and times are formatted in this fashion.

Binary Datatypes Binary datatypes are some of the least compatible (and, fortunately, also some of the least used) datatypes. Unlike all the datatypes explained thus far, which have very specific uses, binary datatypes can contain any data, even binary information, such as graphic images, multimedia, and word processor documents (see Table D.4).

Table D.4

Binary Datatypes

Datatype

Description

BINARY

Fixed-length binary data (maximum length may vary from 255 bytes to 8,000 bytes, depending on implementation)

LONG RAW

Variable-length binary data up to 2GB

RAW (called BINARY by some implementations)

Fixed-length binary data up to 255 bytes

VARBINARY

Variable-length binary data (typically, maximum length varies from 255 bytes to 8,000 bytes, depending on implementation)

217

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Appendix D

Comparing Datatypes If you would like to see a realworld example of database comparisons, look at the table creation scripts used to build the example tables in this book (see Appendix A, “Sample Table Scripts”). By comparing the scripts used for different DBMSs you’ll see first hand just how complex a task datatype matching is.

APPENDIX E

SQL Reserved Words SQL is a language made up of keywords—special words that are used in performing SQL operations. Special care must be taken to not use these keywords when naming databases, tables, columns, and any other database objects. Thus, these keywords are considered reserved. This appendix contains a list of the more common reserved words found in major DBMSs. Please note the following: • Keywords tend to be very DBMS-specific, and not all the keywords that follow are used by all DBMSs. • Many DBMSs have extended the list of SQL reserved words to include terms specific to their implementations. Most DBMSspecific keywords are not listed in the following section. • To ensure future compatibility and portability, it is a good idea to avoid any and all reserved words, even those not reserved by your own DBMS. ABORT

ABSOLUTE

ACTION

ACTIVE

ADD

AFTER

ALL

ALLOCATE

ALTER

ANALYZE

AND

ANY

ARE

AS

ASC

ASCENDING

ASSERTION

AT

AUTHORIZATION

AUTO

AUTO-INCREMENT

AUTOINC

AVG

BACKUP

220

Appendix E

BEFORE

BEGIN

BETWEEN

BIGINT

BINARY

BIT

BLOB

BOOLEAN

BOTH

BREAK

BROWSE

BULK

BY

BYTES

CACHE

CALL

CASCADE

CASCADED

CASE

CAST

CATALOG

CHANGE

CHAR

CHARACTER

CHARACTER_LENGTH

CHECK

CHECKPOINT

CLOSE

CLUSTER

CLUSTERED

COALESCE

COLLATE

COLUMN

COLUMNS

COMMENT

COMMIT

COMMITTED

COMPUTE

COMPUTED

CONDITIONAL

CONFIRM

CONNECT

CONNECTION

CONSTRAINT

CONSTRAINTS

CONTAINING

CONTAINS

CONTAINSTABLE

CONTINUE

CONTROLROW

CONVERT

COPY

COUNT

CREATE

CROSS

CSTRING

CUBE

CURRENT

CURRENT_DATE

CURRENT_TIME

CURRENT_TIMESTAMP

CURRENT_USER

CURSOR

DATABASE

DATABASES

DATE

DATETIME

DAY

DBCC

DEALLOCATE

DEBUG

DEC

DECIMAL

DECLARE

DEFAULT

SQL Reserved Words

DELETE

DENY

DESC

DESCENDING

DESCRIBE

DISCONNECT

DISK

DISTINCT

DISTRIBUTED

DIV

DO

DOMAIN

DOUBLE

DROP

DUMMY

DUMP

ELSE

ELSEIF

ENCLOSED

END

ERRLVL

ERROREXIT

ESCAPE

ESCAPED

EXCEPT

EXCEPTION

EXEC

EXECUTE

EXISTS

EXIT

EXPLAIN

EXTEND

EXTERNAL

EXTRACT

FALSE

FETCH

FIELD

FIELDS

FILE

FILLFACTOR

FILTER

FLOAT

FLOPPY

FOR

FORCE

FOREIGN

FOUND

FREETEXT

FREETEXTTABLE

FROM

FULL

FUNCTION

GENERATOR

GET

GLOBAL

GO

GOTO

GRANT

GROUP

HAVING

HOLDLOCK

HOUR

IDENTITY

IF

IN

INACTIVE

INDEX

INDICATOR

INFILE

INNER

INOUT

INPUT

INSENSITIVE

INSERT

INT

221

222

Appendix E

INTEGER

INTERSECT

INTERVAL

INTO

IS

ISOLATION

JOIN

KEY

KILL

LANGUAGE

LAST

LEADING

LEFT

LENGTH

LEVEL

LIKE

LIMIT

LINENO

LINES

LISTEN

LOAD

LOCAL

LOCK

LOGFILE

LONG

LOWER

MANUAL

MATCH

MAX

MERGE

MESSAGE

MIN

MINUTE

MIRROREXIT

MODULE

MONEY

MONTH

MOVE

NAMES

NATIONAL

NATURAL

NCHAR

NEXT

NEW

NO

NOCHECK

NONCLUSTERED

NONE

NOT

NULL

NULLIF

NUMERIC

OF

OFF

OFFSET

OFFSETS

ON

ONCE

ONLY

OPEN

OPTION

OR

ORDER

OUTER

OUTPUT

OVER

OVERFLOW

OVERLAPS

PAD

PAGE

PAGES

PARAMETER

PARTIAL

PASSWORD

PERCENT

SQL Reserved Words

PERM

PERMANENT

PIPE

PLAN

POSITION

PRECISION

PREPARE

PRIMARY

PRINT

PRIOR

PRIVILEGES

PROC

PROCEDURE

PROCESSEXIT

PROTECTED

PUBLIC

PURGE

RAISERROR

READ

READTEXT

REAL

REFERENCES

REGEXP

RELATIVE

RENAME

REPEAT

REPLACE

REPLICATION

REQUIRE

RESERV

RESERVING

RESET

RESTORE

RESTRICT

RETAIN

RETURN

RETURNS

REVOKE

RIGHT

ROLLBACK

ROLLUP

ROWCOUNT

RULE

SAVE

SAVEPOINT

SCHEMA

SECOND

SECTION

SEGMENT

SELECT

SENSITIVE

SEPARATOR

SEQUENCE

SESSION_USER

SET

SETUSER

SHADOW

SHARED

SHOW

SHUTDOWN

SINGULAR

SIZE

SMALLINT

SNAPSHOT

SOME

SORT

SPACE

SQL

SQLCODE

SQLERROR

STABILITY

STARTING

STARTS

STATISTICS

SUBSTRING

223

224

Appendix E

SUM

SUSPEND

TABLE

TABLES

TAPE

TEMP

TEMPORARY

TEXT

TEXTSIZE

THEN

TIME

TIMESTAMP

TO

TOP

TRAILING

TRAN

TRANSACTION

TRANSLATE

TRIGGER

TRIM

TRUE

TRUNCATE

UNCOMMITTED

UNION

UNIQUE

UNTIL

UPDATE

UPDATETEXT

UPPER

USAGE

USE

USER

USING

VALUE

VALUES

VARCHAR

VARIABLE

VARYING

VERBOSE

VIEW

VOLUME

WAIT

WAITFOR

WHEN

WHERE

WHILE

WITH

WORK

WRITE

WRITETEXT

XOR

YEAR

ZONE

INDEX

SYMBOLS % (percent sign) wildcard, 41-42 [] (square brackets) wildcard, 44-46 ^ (caret) wildcard character, 45 _ (underscore) wildcard, 43-44 | (pipe) symbol, 207 || (double pipes), concatenation operator, 50 ’ (single quotation mark), WHERE clause operators and, 30 * (asterisk) wildcard, queries, 17-18 *= (equality operator), 107 + (concatenation operator), 50 + (plus sign operator), outer joins, 107 , (comma), multiple column separation, 16 @ (at symbol) character, 158 @@ERROR variable, 167 @@IDENTITY global variable, 160

A ABS( ) function, 64 Access (Microsoft) DISTINCT argument support, 72 example tables for, 192

pass-through mode, 198 running, 197-198 sorting by alias, 82 stored procedure support, 154 aggregate functions ALL argument, 72 AVG( ), 67-68 combining, 73-74 COUNT( ), 68-69 defined, 67 DISTINCT argument, 72-73 joins and, 108-110 MAX( ), 69 MIN( ), 70 naming aliases, 74 overview, 66 SUM( ), 71 aliases alternative uses, 54 columns, creating, 101 concatenating fields, 53-54 names, 54 aggregate functions and, 74 self joins, 102-104 table names, 101-102 ALL argument, aggregate functions, 72 ALL clause, grouping data, 77 alphabetical sort order, 23-24

226

SAMS TEACH YOURSELF SQL

IN

ALTER TABLE statements, 139-140 CHECK constraints, 180 CONSTRAINT syntax, 177 syntax, 207 AND keyword, 31 AND operator, 33-34 ANSI SQL, 12 applications filtering query results, 27 SQL compatibility Aqua Data Studio, 195 ColdFusion (Macromedia), 196 DB2 (IBM), 195-196 Microsoft Access, 197-198 Microsoft ASP, 199 Microsoft ASP.NET, 199-200 Microsoft Query, 200-201 Microsoft SQL Server, 201 MySQL, 202 ODBC configuration, 205-206 Oracle, 202 PHP scripting language, 203 PostgreSQL, 203 Query Tool, 203-204 selection criteria, 194 Sybase Adaptive Server, 204-205 Aqua Data Studio running, 195 Web site, 194 arguments ALL, aggregate functions, 72 DBMS support, 73 DISTINCT, aggregate functions, 72-73 AS keyword, 53-54 Oracle support, 102 ASC keyword, query results sort order, 24 ASP (Microsoft), running, 199 ASP.NET (Microsoft), running, 199-200

TEN MINUTES

authentication, 185 authorization, 185 AVG( ) function, 67-68 DISTINCT argument, 72 NULL values, 68

B BETWEEN operator, 63 WHERE clause, 30 between specified values operator (WHERE clause), 28 BINARY datatype, 217 BIT datatype, 215

C calculated fields concatenating fields, 49-52 column aliases, 53-54 mathematical calculations, 55-56 overview, 48-49 subqueries, 88-90 views, 151-152 calculated values, totaling, 71 Cartesian Product, joins and, 95-97 cascading deletes, 178 case sensitivity query result sort order, 25 SQL statements, 16 CHAR string datatype, 214 characters, searching for % (percent sign) wildcard, 41-42 [ ] (square brackets) wildcard, 44-46 _ (underscore) wildcard, 43-44 check constraints, 179-180 clauses, 20 ALL, grouping data, 77 GROUP BY, 76-77 HAVING, grouping data, 78

INDEX

IS NULL, 31 SELECT statements, order of, 83 WHERE, 26 AND operator, 33-34 checking against single value, 29 checking for nonmatches, 29-30 checking for NULL value, 31 checking for range of values, 30-31 IN operator, 36-38 joins and, 95-97 multiple query criteria, 33 NOT operator, 38-39 operator support by DBMS, 30 operators, 28 OR operator, 34-35 order of evaluation, 35-36 positioning, 27 SOUNDEX function, 61 client-based results formatting, compared to server-based, 49 CLOSE statements, closing cursors, 173 code (programming) commenting, 59 stored procedures, 159 portability, 58 ColdFusion (Macromedia), running, 196 columns. See also fields aliases alternative uses, 54 concatenating fields, 53-54 creating, 101 names, 54 AVG( ) function, individual columns, 68 breaking data correctly, 8 concepts, 8 Customers example table, 189

derived, 54 fully qualified names, 95 GROUP BY clause, 77 grouping data, specifying by relative position, 77 Identity fields, 160 INSERT SELECT statements, 124 INSERT statement, omitting columns, 122 insert STATEMENT AND, 120 multiple, sorting query results by, 21-22 nonselected, sorting query results by, 23 NULL value, checking for, 31 NULL value columns, 136-137 OrderItems example table, 191 Orders example table, 190 padded spaces, RTRIM( ) function, 51-52 position, sorting query results by, 22 primary keys, 10 Products example table, 189 retrieving all, 17-18 individual, 14-15 multiple, 16-17 unknown, 18 separating names in queries, 16 sorting data, descending on multiple columns, 24 subquery result restrictions, 88 updating multiple, 128 values, deleting, 129 Vendors example table, 188 combined queries creating, 112-113 duplicate rows and, 114-115 overview, 111 performance, 114 rules, 114 sorting results, 116

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227

228

SAMS TEACH YOURSELF SQL

IN

Command Center utility, running, 195-196 commas (,), multiple column separation, 16 commenting programming code, importance of, 59 stored procedure code, 159 COMMIT statement (transaction processing), 165 commits defined, 163 syntax, 208 compatibility datatypes, 9 functions, DBMS support considerations, 57-58 WHERE clause operators, 28 compatibility (SQL code), application selection criteria, 194 concatenating fields, 49-52 column aliases, 53-54 mathematical calculations, 55-56 MySQL, 51 concatenation operators, 50 configuring ODBC, 205-206 CONSTRAINT syntax, ALTER TABLE statements, 177 constraints (referential integrity) check constraints, 179-180 foreign keys, 176-177 overview, 174-175 primary keys, 175-176 speed, 184 unique constraints, 178-179 copying tables, 126 COS( ) function, 64 COUNT( ) function, 67-69 DISTINCT argument, 73 joins and, 108 NULL values, 69 COUNT* subquery, 89 CREATE INDEX statement, 182 syntax, 208

TEN MINUTES

CREATE TABLE statement, 133-135 DEFAULT keyword, 137-138 syntax, 208 CREATE VIEW statement, 146 syntax, 209 cross joins, 97 currency datatypes, 216 cursors accessing, 171-173 closing, 173 creating, 170-171 implementing, 170 limitations, 169 opening, 171 options, support for, 168 overview, 168 Web-based applications, 169 Customers table, 189

D data breaking correctly (columns), 8 deleting guidelines, 131 TRUNCATE TABLE statement, 131 filtering, indexes and, 182 manipulation functions, date and time, 63 security, 185 updating, guidelines, 131 Database Management System. See DBMS databases. See also tables concepts, 5-6 defined, 6 dropping objects, 209 indexes cautions, 182 creating, 182 scalability, 93 schemas, 7

INDEX

security, 185 tables creating, 208 triggers, 183 DATALENGTH( ) function, 60 datatypes, 8 binary, 217 compatibility, 9 currency, 216 data and time, 216 defining, 180 numeric, 215 string, 213 usefulness of, 212 BIT, 215 CHAR, 217 compatibility, 9 currency, 216 DATE, 216 DATETIME, 216 DBMS differences, 213 DECIMAL, 215 defining, 180 INT, 215 NCHAR, 214 NVARCHAR, 214 TEXT, 214 TINYINT, 215 user-defined, 180 VARBINARY, 217 date (system), default value syntax, 138 date and time datatypes, 216 date and time functions, 59, 62-64 DATE datatype, 216 DATEPART( ) function, 62 DATETIME datatype, 216 DB2 (IBM), running, 195-196 DBMS (Database Management System), 6 accidental table deletion, 141 datatype differences, 213 functions, support considerations, 57-58 indexes, 182

interactive tools, 93 ISAM databases, 169 LIKE operator, search patterns and, 41 NULL value differences, 137 query sort order, 20 security mechanisms, 185 SQL extensions, 12 transaction processing, implementation differences, 164 triggers, 184 TRIM functions, 52 UNION statements, 117 user-defined datatypes, 180 view creation, 144 views, rules and restrictions, 145 WHERE clause, allowed operators, 32 DECIMAL datatype, 215 DECLARE statements cursors, creating, 170-171 stored procedures, 158 default values, tables, 137-138 defining datatypes, 180 DELETE FROM statements, 130 DELETE statement, 129-130 FROM keyword, 130 guidelines, 131 security privileges, 130 syntax, 209 transaction processing, 163 TRUNCATE TABLE statement, 131 DELETE statements rollbacks, 165 triggers, 183 WHERE clause, 130 deleting column values, 129 data guidelines, 131 TRUNCATE TABLE statement, 131

How can we make this index more useful? Email us at [email protected]

229

230

SAMS TEACH YOURSELF SQL

IN

rows, 209 preventing accidental, 178 tables, 141 preventing accidental, 141 derived columns. See aliases DESC keyword, query results sort order, 23-24 dictionary sort order (query results), 25 DISTINCT argument AVG( ) function, 72 COUNT( ) function, 73 double pipes (||), concatenation operator, 50 downloading example tables, 191 Microsoft Access MDB file, 192 SQL scripts, 192 DROP statement, syntax, 209 DROP TABLE statement, 141 dropping database objects, 209

E empty strings, compared to NULL values, 137 equality (*=) operator, 107 equality operator (WHERE clause), 28 establishing primary keys, 10 example tables Customers table, 189 downloading, 191 functions of, 187 Microsoft Access MDB file, 192 OrderItems table, 191 Orders table, 190 Products table, 189 SQL scripts, 192 Vendors table, 188 EXCEPT statement, 117 EXECUTE statement, stored procedures, 156-160

TEN MINUTES

EXP( ) function, 64 explicit commits, 165 extensions, 12

F FETCH statement, accessing cursors, 171-173 fields. See also calculated fields; columns aliases, names, 54 calculated concatenating fields, 49-54 mathematical calculations, 55-56 overview, 48-49 subqueries, 88-90 views, 151-152 filtering % (percent sign) wildcard, 41-42 [] (square brackets) wildcard, 44-46 _ (underscore) wildcard, 43-44 data, indexes, 182 data groups, 78-80 LIKE operator, 40-41 query results, 26 AND operator, 33-34 application level, 27 IN operator, 36-38 multiple criteria, 33 NOT operator, 38-39 OR operator, 34-35 order of evaluation, 35-36 WHERE clause operators, 28-31 by subqueries, 84-88 with views, 150 fixed length strings, 213 FLOAT datatype, 215 foreign keys, 176-177

INDEX

formatting query data, 17 retrieved data with views, 148-149 server-based compared to client-based, 49 statements, 135 subqueries, 87 FROM clause, creating joins, 94 FROM keyword, 14 DELETE statement, 130 UPDATE statement, 129 full outer joins, 108 fully qualified column names, 95 functions ABS( ), 64 advisability of using, 59 aggregate ALL argument, 72 AVG( ), 67-68 combining, 73-74 COUNT( ), 68-69 defined, 67 DISTINCT argument, 72-73 joins and, 108-110 MAX( ), 69 MIN( ), 70 naming aliases, 74 overview, 66 SUM( ), 71 AVG( ), 67-68 DISTINCT argument, 72 NULL values, 68 COS( ), 64 COUNT( ), 67-69 DISTINCT argument, 73 NULL values, 69 DATALENGTH( ), 60 date and time, 59, 62-64 DATEPART( ), 62 defined, 57 EXP( ), 64 LCASE( ), 60 LEFT( ), 60

LEN( ), 60 LENGTH( ), 60 LOWER( ), 60 LTRIM( ), 60 MAX( ), 67-69 DISTINCT argument, 73 non-numeric data, 70 NULL values, 70 MIN( ), 67, 70 DISTINCT argument, 73 non-numeric data, 70 NULL values, 71 numeric, 59, 64-65 PI( ), 65 RIGHT( ), 60 RTRIM( ), 51-52, 60 SIN( ), 65 SOUNDEX( ), 60-61 support for, 61 SQRT( ), 65 SUM( ), 67, 71 multiple columns and, 72 NULL values, 72 support considerations, 57-58 system, 59 TAN( ), 65 text, 59-60 list of common, 60 to_char, 63 to_number, 63 TRIM, 52 types of, 59 UCASE( ), 60 UPPER( ), 59-60 YEAR( ), 63

G global variables, @@IDENTITY, 160 GRANT statement, 185 greater than operator (WHERE clause), 28

How can we make this index more useful? Email us at [email protected]

231

232

SAMS TEACH YOURSELF SQL

IN

greater than or equal to operator (WHERE clause), 28 GROUP BY clause, 76-77 compared to ORDER BY clause, 80-82 grouping data, 75 columns, specifying by position, 77 compared to sorting, 80-82 filtering groups, 78-80 GROUP BY clause, 76-77 nested groups, 76 operators, 35

H-I HAVING clause, grouping data, 78 IBM DB2, running, 195-196 Identity fields, 160 IN operator, 36-38 indexes cautions, 182 creating, 182, 208 overview, 180-182 revisiting, 182 inner joins, 97-98 INSERT SELECT statement, 122-124 SELECT INTO statement comparison, 125 syntax, 210 INSERT statement columns lists, 121 completing rows, 118-120 INTO keyword, 119 omitting columns, 122 overview, 118 partial rows, 121-122 query data, 122-124 rollbacks, 167 security privileges, 118 syntax, 209

TEN MINUTES

transaction processing, 163 triggers, 183 VALUES, 121 INT datatype, 215 integrity. See referential integrity interactive DBMS tools, 93 INTERSECT statements, 117 INTO keyword, 119 IS NULL clause, 31 ISAM (Indexed Sequential Access Method) databases, 169 ISTINCT argument, aggregate functions, 72-73

J-K joins aggregate functions and, 108-110 Cartesian Product, 95-97 creating, 94 cross joins, 97 inner joins, 97-98 multiple tables, 98-100 natural joins, 104-105 outer, 105-108 full, 108 left, 107 right, 107 syntax, 107 types, 108 overview, 91 performance considerations, 99 self joins, 102-104 usefulness of, 93 views, 147-148 WHERE clause, 95-97 keys, primary, 9-11 keywords, 13 AND, 31, 34 AS, 53-54 Oracle support, 102 ASC, query results sort order, 24

INDEX

DEFAULT, table values, 137-138 DESC, query results sort order, 23-24 FROM, 14, 129 IN, 38 INTO, 119 NOT, 38 OR, 35 REFERENCES, 177 UNIQUE, 179

L languages, SQL, 11 LCASE( ) function, 60 LEFT keyword (outer joins), 106 left outer joins, 107 LEFT( ) function, 60 LEN( ) function, 60 LENGTH( ) function, 60 less than operator (WHERE clause), 28 less than or equal to operator (WHERE clause), 28 LIKE operator, 40-41 % (percent sign) wildcard, 41-42 [] (square brackets), 44-46 _ (underscore) wildcard, 43-44 local variables, @ character, 158 logical operators, defined, 33 LONG RAW datatype, 217 LOWER( ) function, 60 LTRIM( ) function, 52, 60

M Macromedia ColdFusion, running, 196 mathematical calculations, 55-56 operators, 56

MAX( ) function, 67-69 DISTINCT argument, 73 non-numeric data, 70 NULL values, 70 Microsoft Access DISTINCT argument support, 72 example tables for, 192 pass-through mode, 198 running, 197-198 sorting by alias, 82 stored procedure support, 154 Microsoft ASP, running, 199 Microsoft ASP.NET, running, 199-200 Microsoft Query, running, 200-201 Microsoft SQL Server, running, 201 MIN( ) function, 67, 70 DISTINCT argument, 73 non-numeric data, 70 NULL values, 71 MySQL concatenation, 51 cursor support, 168 NOT operator, 39 running, 202 stored procedure support, 154 subquery support, 84 views, support for, 143

N naming aliases, 54 aggregate functions and, 74 columns, fully qualified names, 95 indexes, 182 tables, 7 reserved words and, 13 aliases, 101-102 natural joins, 104-105 navigating tables, cursors, 168

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233

234

SAMS TEACH YOURSELF SQL

NCHAR string datatype, 214 nested data groups, 76 non-equality operator (WHERE clause), 28 non-numeric data MAX( ) function, 70 MIN( ) function, 70 not greater than operator (WHERE clause), 28 not less than operator (WHERE clause), 28 NOT operator, 38-39 character searching and, 46 NULL keyword, updating columns, 129 NULL value operator (WHERE clause), 28 NULL values AVG( ) function, 68 checking for, 31 compared to empty strings, 137 COUNT( ) function, 69 MAX( ) function, 70 MIN( ) function, 71 primary keys, 137 SUM( ) function, 72 table columns, 136-137 numeric datatypes, 215 functions, 59, 64-65 values, quotes, 215 NVARCHAR string datatype, 214

O ODBC configuration, 205-206 dates, 217 OPEN CURSOR statement, 171 OPEN statement, opening cursors, 173

IN

TEN MINUTES

operators *= (equality), 107 % (percent sign) wildcard, 41-42 + (plus sign), outer joins, 107 [] (square brackets) wildcard, 44-46 _ (underscore) wildcard, 43-44 AND, 33-34 BETWEEN, 63 concatenation, 50 defined, 33 grouping related, 35 HAVING clause, 78 IN, 36-38 LIKE, 40-41 mathematical, 56 NOT, 38-39 OR, 34-35 order of evaluation, 35-36 predicates, 41 WHERE clause, 28 checking against single value, 29 checking for nonmatches, 29-30 checking for NULL value, 31 checking for range of values, 30-31 compatibility, 28 OR operator, 34-35 Oracle commits, 165 cursors closing, 173 creating, 170 retrieving data, 171 date and time manipulation functions, 63 date formatting, 64 running, 202 savepoints, 166 stored procedures, 157 triggers, 184

INDEX

ORDER BY clause (SELECT statement), 20 ascending/descending sort order, 23-24 compared to GROUP BY clause, 80-82 positioning, 20 sorting by column position, 22 sorting by multiple columns, 21-22 sorting by nonselected columns, 23 OrderItems table, 191 Orders table, 190 outer joins, 105-108 full, 108 left, 107 right, 107 syntax, 106-107 types, 108 overwriting tables, 135

P parentheses, multiple query criteria order of evaluation, 35 pass-through mode (Microsoft Access), 198 patterns (searching), wildcards, 40-41 % (percent sign), 41-42 [] (square brackets), 44-46 _ (underscore), 43-44 percent sign (%) wildcard, 41-42 performance combining queries, 114 deleting data, 131 indexes, 181 joins and, 99 subqueries, 88 views, 145 PHP scripting language, running, 203 PI( ) function, 65

pipe (|) symbol, 207 placeholders. See savepoints plus sign (+) concatenation operator, 50 outer joins, 107 portability defined, 58 INSERT statements and, 121 PostgreSQL filter query data, 27 running, 203 predicates (operators), 41 primary keys, 175-176 concepts, 9-11 Customer example table, 190 importance, 10 NULL values, 137 OrderItems example table, 191 Orders example table, 190 Products example table, 189 Vendors example table, 188 processing subqueries, 86 transactions, 161 Products table, 189 programming code commenting, 59 portability, 58

Q queries calculated fields concatenating fields, 49-54 mathematical calculations, 55-56 overview, 48-49 combined creating, 112-113 duplicate rows and, 114-115 overview, 111 performance, 114 rules, 114

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235

236

SAMS TEACH YOURSELF SQL

IN

sorting results, 116 WHERE clauses, 111 combining, 86 data formatting, 17 defined, 84 filtering results, 26 AND operator, 33-34 IN operator, 36-38 multiple criteria, 33 NOT operator, 38-39 OR operator, 34-35 order of evaluation, 35-36 WHERE clause operators, 28-31 INSERT statement and, 122-124 multiple WHERE clauses, 113 result sets, 168 sorting results, 19-20 ascending/descending order, 23-24 by column position, 22 by multiple columns, 21-22 by nonselected columns, 21, 23 case sensitivity, 25 subqueries as calculated fields, 88-90 filtering by, 84-88 overview, 84 processing, 86 self joins and, 103 table aliases, 102 unsorted results, 15 views, 144 wild cards (*), 17-18 Query (Microsoft), running, 200-201 Query Tool running, 203-204 Web site, 194 quotation marks numeric values, 215 single (‘), 30 string values, 214

TEN MINUTES

R RAW datatype, 217 REAL datatype, 215 records, compared to rows, 9 REFERENCES keyword, 177 referential integrity cascading deletes, 178 constraints check constraints, 179-180 foreign keys, 176-177 overview, 174-175 primary keys, 175-176 unique constraints, 178-179 natural joins, 104-105 outer joins, 105-108 self joins, 102-104 reformatting retrieved data with views, 148-149 relational databases (DBMS) nonrelational behavior, inducing, 169 sort order and, 20 relational tables, 91-92 relationships constraints check constraints, 179-180 foreign keys, 176-177 overview, 174-175 primary keys, 175-176 unique constraints, 178-179 natural joins, 104-105 outer joins, 105-108 self joins, 102-104 RENAME statement, 141 renaming tables, 141 reserved words, 13, 219 list of, 220-224 restrictions, views, 145-146 result sets, 168 reusable views, creating, 148 revisiting indexes, 182

INDEX

REVOKE statements, 185 RIGHT keyword (outer joins), 106 right outer joins, 107 RIGHT( ) function, 60 ROLLBACK command (transaction processing), 164-165 ROLLBACK statement, syntax, 210 rollbacks (transaction processing), 163 COMMIT statement, 165 defined, 163 ROLLBACK command, 164-165 savebacks and, 166-167 statements, 167 rows adding to tables, 209 compared to records, 9 concepts, 9 cursors, 168 deleting, 209 INSERT statement, 118-120 partial rows, 121-122 preventing accidental deletion, 178 updating, 211 RTRIM( ) function, 51-52, 60 rules combining queries, 114 constraints, 175 views, 145-146

S savepoints (transaction processing), 166-167 defined, 163 scalablity, 93 schemas, 7 scripting, PHP, 203 scripts, example tables, 192

search patterns defined, 40 wildcards, 40-41 % (percent sign) wildcard, 41-42 [] (square brackets) wildcard, 44-46 _ (underscore) wildcard, 43-44 cautions, 46 searching indexes, overview, 180-182 trailing spaces and, 43 wildcards ^ (caret) character, 45 % character, 41-42 [] (square brackets), 44-46 _ (underscore), 43-44 security data, 185 DELETE statement, 130 INSERT statements, 118 UPDATE statement, 127 SELECT INTO statement, 125 INSERT SELECT statement comparison, 125 SELECT statement, 13 aggregate functions, combining, 73-74 AS keyword, 53-54 AVG( ) function, 67 clauses, ordering of, 83 columns retrieving all, 17-18 retrieving individual, 14-15 retrieving multiple, 16-17 retrieving unknown, 18 combining creating, 112-113 duplicate rows and, 114-115 overview, 111 rules, 114 sorting results, 116 concatenating fields, 50-51

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237

238

SAMS TEACH YOURSELF SQL

IN

COUNT( ) function, 69 FROM clause, creating joins, 94 grouping data, 76-77 IS NULL clause, 31 ORDER BY clause, 20 positioning, 20 syntax, 210 SELECT statements subqueries, formatting, 87 WHERE clause, 26 WHERE clauses combined queries, 111 combining, 33 NOT operator, 38 self joins, 102-104 compared to subqueries, 104 semicolons (;), multiple statements, 16 sequence (SELECT statement clauses), 83 server-based results formatting, compared to client-based, 49 SET command, updating tables, 128 SIN( ) function, 65 single quotation marks (‘) WHERE clause operators and, 30 SMALLDATETIME datatype, 216 SMALLINT datatype, 215 sorting combined query results, 116 compared to grouping, 80-82 datatype functionality, 212 indexes, overview, 180-182 query results, 19-20 ascending/descending order, 23-24 by column position, 22 by multiple columns, 21-22 by nonselected columns, 21-23 case sensitivity, 25 SOUNDEX( ) function, 60-61 support for, 61

TEN MINUTES

spaces removing, RTRIM function, 51-52 search results and, 43 specifying dates, 216 speed, constraints versus triggers, 184 SQL deleting/updating data, 132 extensions, 12 overview, 11 scripts, example tables, 192 SQL Server cursors, closing, 173 Identity fields, 160 local variables, @ character, 158 running, 201 savepoints, 166 stored procedures, 158 triggers, 184 SQRT( ) function, 65 square brackets ([]) wildcard, 44-46 statements ALTER TABLE, 139-140 syntax, 207 case sensitivity, 16 clauses, 20 COMMIT, 165 syntax, 208 CREATE INDEX, 182 syntax, 208 CREATE TABLE, 133-135 syntax, 208 CREATE VIEW, 146 syntax, 209 DELETE, 129-131 FROM keyword, 130 syntax, 209 transaction processing, 163 DROP, syntax, 209 DROP TABLE, 141 formatting, 135 GRANT, 185 grouping related operators, 35

INDEX

INSERT completing rows, 118-120 omitting columns, 122 overview, 118 partial rows, 121-122 query data, 122-124 security privileges, 118 syntax, 209 transaction processing, 163 VALUES, 121 INSERT SELECT, syntax, 210 multiple, separating, 16 OPEN CURSOR, 171 RENAME, 141 REVOKE, 185 ROLLBACK, syntax, 210 rollbacks, 163, 167 defined, 163 SELECT, 13 AVG( ) function, 67 combining, 111-116 combining aggregate functions, 73-74 concatenating fields, 50-51 COUNT( ) function, 69 grouping data, 76-77 retrieving all columns, 17-18 retrieving individual columns, 14-15 retrieving multiple columns, 16-17 retrieving unknown columns, 18 syntax, 210 stored procedures creating, 157-160 disadvantages of, 155-156 executing, 156-157 overview, 153-154 usefulness of, 154-155 syntax, 207-211

UPDATE, 127-131 syntax, 211 transaction processing, 163 white space, 15 stored procedures commenting code, 159 creating, 157-160, 208 disadvantages of, 155-156 executing, 156-157 Identity fields, 160 Oracle, 157 overview, 153-154 triggers, 183 usefulness of, 154-155 storing date and time values, 216 numeric values, cautions, 214 strings, 213 string datatypes, 213 strings empty, compared to NULL values, 137 fixed length, 213 quotes, 214 TRIM functions, 52 variable-length, 213 wildcard searching and, 41 subqueries as calculated fields, 88-90 compared to self joins, 104 COUNT*, 89 filtering by, 84-88 formatting, 87 multiple columns, 72 NULL values, 72 overview, 84 performance, 88 processing, 86 self joins and, 103 SUM( ) function, 67, 71 UPDATE statement, 129 WHERE clauses, 88 support, DBMS function support, 57-58

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239

240

SAMS TEACH YOURSELF SQL

IN

Sybase Adaptive Server running, 204-205 statements, ending, 16 syntax ALTER TABLE statements, 207 column aliases, 101 COMMIT statement, 208 CREATE INDEX statement, 208 CREATE TABLE statement, 133, 208 CREATE VIEW statement, 209 DELETE statement, 209 DROP statement, 209 INERT statement, 210 INSERT statement, 209 outer joins, 106 ROLLBACK statement, 210 SELECT statement, 210 statements, 207-211 transaction processing, 164 triggers, 184 UPDATE statement, 211 system date, default value syntax, 138 system functions, 59

T tables calculated fields concatenating fields, 49-54 mathematical calculations, 55-56 overview, 48-49 columns, 8 aliases, creating, 101 NULL value, checking for, 31 primary keys, 10 concepts, 7 copying, 126

TEN MINUTES

copying data into tables, 124-126 creating, 208 CREATE TABLE statement, 134-135 overview, 133-134 datatypes, 8 default values, 137-138 deleting, 141 preventing accidental deletion, 141 examples Customers table, 189 downloading, 191 indexes cautions, 182 creating, 182 searching, 181 INSERT statement, multiple rows, 124 inserting data, 118-120 partial rows, 121-122 from queries, 122-124 joins Cartesian Product, 95-97 creating, 94 cross joins, 97 inner joins, 97-98 multiple tables, 98-100 overview, 91 performance considerations, 99 usefulness of, 93 WHERE clause, 95-97 Microsoft Access MDB file, 192 naming, 7 reserved words and, 13 natural joins, 104-105 NULL value columns, 136-137 outer joins, 105-108 relational, 91-92 renaming, 141 replacing, 135

INDEX

rows, 9 adding, 209 deleting, 209 updating, 211 schemas, 7 security, 185 SQL scripts, 192 functions of, 187 OrderItems table, 191 Orders table, 190 Products table, 189 Vendors table, 188 table name aliases, 101-102 self joins, 102-104 triggers, 183 creating, 184 functionality, 183 updating, 127-129, 139-140 deleting data, 129-130 views, creating, 209 TAN( ) function, 65 testing, Query Tool and, 203-204 text functions, 59-60 list of common, 60 TEXT string datatype, 214 time functions, 59, 62-64 TINYINT datatype, 215 tools (DBMS), interactive, 93 TOP argument, 73 TOP PERCENT argument, 73 totaling values calculated values, 71 SUM( ) function, 71 to_char( ) function, 63 to_number( ) function, 63 transaction processing, 166-167 blocks, ROLLBACK statements, 210 COMMIT command, 165 defined, 163 explicit commits, 165 managing, 164 overview, 161-162 ROLLBACK command, 164-165

terminology, 163 writing to databases, 208 triggers, 183 creating, 184 functionality, 183 overview, 183-184 speed, 184 syntax examples, 184 TRIM( ) function, 52 trimming padded spaces, 51-52 troubleshooting accidental table deletion, 141 Query Tool and, 203-204 TRUNCATE TABLE statement, 131

U UCASE( ) function, 60 underscore (_) wildcard, 43-44 UNION operator combined queries, 112-113 compared to WHERE clauses, 115 duplicate rows and, 114-115 rules, 114 sorting results, 116 limits, 113 UNION statements, types, 117 unions (queries) creating, 112-113 duplicate rows and, 114-115 overview, 111 rules, 114 sorting results, 116 unique constraints, 178-179 UNIQUE keyword, 179 unsorted data, query results, 15 UPDATE statement, 127-129 FROM keyword, 129 guidelines, 131 security privileges, 127 subqueries, 129 syntax, 211

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241

242

SAMS TEACH YOURSELF SQL

IN

transaction processing, 163 triggers, 183 updating data, guidelines, 131 tables, 127-129, 139-140 deleting data, 129-130 UPPER( ) function, 59-60 user-defined datatypes, 180

V values concatenation, 50 searching for (indexes), 181 trimming padded space, 52 VARBINARY datatype, 217 variable-length strings, 213 Vendors table, 188 views (tables) calculated fields, 151-152 creating, 145 creating, 209 DBMS consistency, 144 filtering data, 150 joins, simplifying, 147-148 overview, 143-146 performance concerns, 145 reformatting retrieved data, 148-149 reusable, 148 rules and restrictions, 145-146 usefulness of, 144-145 virtual tables. See views

W-X-Y-Z Web sites Aqua Data Studio, 194 example table download site, 191 Query Tool, 194, 204 Web-based applications, cursors, 169

TEN MINUTES

WHERE clause, 26. See also HAVING clause BETWEEN operator, 30 combining in queries, 111 compared to UNION statement, 115 DELETE statement, 130 filtering groups, 79 joins, 97 joins and, 95-97 multiple query criteria, 33 AND operator, 33-34 IN operator, 36-38 NOT operator, 38-39 OR operator, 34-35 order of evaluation, 35-36 NOT operator, 38 operators, 28 checking against single value, 29 checking for nonmatches, 29-30 checking for NULL value, 31 checking for range of values, 30-31 quotes and, 30 operators support by DBMS, 30 parentheses and, 36 positioning, 27 SOUNDEX( ) function, 61 subqueries, 87 UPDATE statements, 127-128 wildcards, 40 white space, SQL statements, 15 wildcards asterisk (*) character, 17-18 caret (^) character, 45 cautions, 46 defined, 40 LIKE operator and, 40-41 natural joins, 105 writing stored procedures, 155 YEAR( ) function, 63

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